Predicting_Heart_Disease.ipynb

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        "<a href=\"https://colab.research.google.com/github/The-PhanTom999/Predicting_Heart_Disease/blob/main/Predicting_Heart_Disease.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Predicting Heart Disease"
      ],
      "metadata": {
        "id": "XWOeDtV9zDeA"
      }
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      "cell_type": "markdown",
      "source": [
        "## Introduction \n",
        "\n",
        "\n",
        "According to the [World Health Organization (WHO)](https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1), approximately 17.9 million individuals die each year due to cardiovascular diseases (CVDs).\n",
        "\n",
        "There are several risk factors that could contribute to an individual's likelihood of developing CVD, such as an unhealthy diet, lack of physical activity, or mental illnesses. Identifying these risk factors in individuals early on could potentially prevent numerous premature deaths.\n",
        "\n",
        "The goal of this project is to create a K-Nearest Neighbors classifier that can accurately predict the likelihood of a patient developing heart disease in the future, utilizing this [Kaggle dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction). This dataset combines five different heart datasets over 11 common features, making it the most extensive heart disease dataset available for research purposes.\n"
      ],
      "metadata": {
        "id": "Z0Q2PTn19Lnp"
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    {
      "cell_type": "markdown",
      "source": [
        "I'll start by importing the required libaries: "
      ],
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    {
      "cell_type": "code",
      "source": [
        "# importing libraries\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "TMnb8_Vl1MA4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration"
      ],
      "metadata": {
        "id": "8p5k6yPP2CnF"
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      "cell_type": "markdown",
      "source": [
        "From the dataset documentation, it has the following features:\n",
        "\n",
        "* Age: age of the patient [years]\n",
        "* Sex: sex of the patient [M: Male, F: Female]\n",
        "* ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\n",
        "* RestingBP: resting blood pressure [mm Hg]\n",
        "* Cholesterol: serum cholesterol [mm/dl]\n",
        "* FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\n",
        "* RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\n",
        "* MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]\n",
        "* ExerciseAngina: exercise-induced angina [Y: Yes, N: No]\n",
        "* Oldpeak: oldpeak = ST [Numeric value measured in depression]\n",
        "* ST_Slope: the slope of the peak exercise * ST segment [Up: upsloping, Flat: flat, Down: downsloping]\n",
        "* HeartDisease: output class [1: heart disease, 0: Normal]\n",
        "\n"
      ],
      "metadata": {
        "id": "ABbSS3VV24jO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now load the dataset"
      ],
      "metadata": {
        "id": "orlX_sR94235"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading dataset\n",
        "df = pd.read_csv(\"heart.csv\")\n",
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
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          "data": {
            "text/plain": [
              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0              N      0.0       Up             0  \n",
              "1              N      1.0     Flat             1  \n",
              "2              N      0.0       Up             0  \n",
              "3              Y      1.5     Flat             1  \n",
              "4              N      0.0       Up             0  "
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              "      <td>ST</td>\n",
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              "      <th>3</th>\n",
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      "cell_type": "markdown",
      "source": [
        "I'm now using the info() method to find more information about the dataset"
      ],
      "metadata": {
        "id": "oWZRtRO35U43"
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      "cell_type": "code",
      "source": [
        "df.info()"
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        },
        "outputId": "82019ce5-390b-41fc-9211-11097168ad9f"
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      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "source": [
        "From the output above, we can see that :\n",
        "* The dataset has 918 rows and 12 columns.\n",
        "* 7 features in total are numerical while 5 are categorical. However, two of the numerical features, FastingBS and HeartDisease are categorical as well.\n",
        "* There are no missing values in this dataset!\n"
      ],
      "metadata": {
        "id": "H1PucrNZ6Qm9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Focusing on the Numerical Columns, I will use Pandas `.describe()` method to view some basic statistical details about them: "
      ],
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        "id": "00dlYXTI7yn-"
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      "cell_type": "code",
      "source": [
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            "text/plain": [
              "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
              "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
              "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
              "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
              "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
              "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
              "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
              "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
              "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
              "\n",
              "          Oldpeak  HeartDisease  \n",
              "count  918.000000    918.000000  \n",
              "mean     0.887364      0.553377  \n",
              "std      1.066570      0.497414  \n",
              "min     -2.600000      0.000000  \n",
              "25%      0.000000      0.000000  \n",
              "50%      0.600000      1.000000  \n",
              "75%      1.500000      1.000000  \n",
              "max      6.200000      1.000000  "
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              "      <th>Age</th>\n",
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              "      <th>Cholesterol</th>\n",
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              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
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              "      <td>0.233115</td>\n",
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              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
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              "      <td>1.066570</td>\n",
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              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21137b96-a831-45f2-9f81-b9ea163c0b5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21137b96-a831-45f2-9f81-b9ea163c0b5a 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-21137b96-a831-45f2-9f81-b9ea163c0b5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the table above, we can observe that:\n",
        "\n",
        "* The average age of patients is approximately 53 years.\n",
        "* The median for Cholesterol is higher than its mean by roughly 25 mm/dl, indicating that it could be a left-skewed distribution with a possibility of outliers skewing the distribution.\n",
        "*RestingBP and Cholesterol have a minimum value of zero."
      ],
      "metadata": {
        "id": "qd3IWYQd8n0r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, a couple of observations stand out. \n",
        "* `RestingBP` is the resting blood pressure, and it's unlikely to be zero.\n",
        "* As per the dataset description, Cholesterol contains the serum cholesterol values. And per the [American Heart Association](https://www.heart.org/en/health-topics/cholesterol/about-cholesterol/what-your-cholesterol-levels-mean), serum cholesterol is a composite of different measurements. Your total blood cholesterol is calculated by adding your HDL and LDL cholesterol levels, plus 20% of your triglyceride level. With the above information is enough to say that a 0 value for serum cholesterol is unlikely.\n",
        "\n",
        "Next,I'll explore the categorical features, relying on visualizations to understand how the data is distributed."
      ],
      "metadata": {
        "id": "m8HYXLX2scQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = [\"Sex\", \"ChestPainType\", \"FastingBS\", \"RestingECG\", \"ExerciseAngina\", \"ST_Slope\", \"HeartDisease\"]\n",
        "\n",
        "fig = plt.figure(figsize=(16,15))\n",
        "\n",
        "for idx, col in enumerate(categorical_cols[:-1]):\n",
        "    ax = plt.subplot(3, 2, idx+1)\n",
        "    # group by HeartDisease\n",
        "    sns.countplot(x=df[col], hue=df[\"HeartDisease\"], ax=ax)\n",
        "    # add data labels to each bar\n",
        "    for container in ax.containers:\n",
        "        ax.bar_label(container, label_type=\"center\")"
      ],
      "metadata": {
        "id": "pq_CfAMAv7p-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "outputId": "942124ba-1611-4a05-d2a6-14a9bef5f5eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1500 with 6 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the data distribution from the above plots:\n",
        "* We can further notice how skewed the dataset is towards male patients. Only 50 female patients in the dataset have been diagnosed with heart disease.\n",
        "* A significant number of patients, 392, diagnosed with heart disease have asymptomatic (ASY) chest pain. While chest pain could be a relevant feature for our model, asymptomatic implies that those patients who had a heart disease did not have chest pain as a symptom.\n",
        "* A high number (170) of patients with blood sugar greater than 120 mg/dl were diagnosed with heart disease in relation to those who were not diagnosed as such.\n",
        "* Out of all patients who had an exercise-induced angina, 316 were diagnosed with a heart disease.\n",
        "* Out of all patients with a flat ST slope, 381 were diagnosed with a heart disease.\n",
        "\n",
        "With this, we can start to identify some features that could be relevant to us, But let's clean up the dataset a bit first before narrowing down on our features."
      ],
      "metadata": {
        "id": "rlVNB7MewKwt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Cleaning"
      ],
      "metadata": {
        "id": "BXbm8gME8-dw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we identified earlier, there are no missing values, but a couple of columns have 0 values that don't make sense.\n",
        "Lets look at how many zero values RestingBP and Cholesterol contain and decide how to handle those."
      ],
      "metadata": {
        "id": "tJcxXAJAzH1G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"RestingBP\"] == 0]"
      ],
      "metadata": {
        "id": "Pm0jVE6azt0T",
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              "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
              "449   55   M           NAP          0            0          0     Normal   \n",
              "\n",
              "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
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        " df[df[\"Cholesterol\"] == 0]"
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              "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
              "293   65   M           ASY        115            0          0     Normal   \n",
              "294   32   M            TA         95            0          1     Normal   \n",
              "295   61   M           ASY        105            0          1     Normal   \n",
              "296   50   M           ASY        145            0          1     Normal   \n",
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              "514   43   M           ASY        122            0          0     Normal   \n",
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              "536   62   M           NAP        133            0          1         ST   \n",
              "\n",
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              "293     93              Y      0.0     Flat             1  \n",
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              "535    122              Y      1.0     Flat             1  \n",
              "536    119              Y      1.2     Flat             1  \n",
              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`RestingBP` has only one zero value so we will be removing that row from consideration.\n",
        "However, there are 172 zero values for `Cholesterol` that's a relatively high number and we can't remove them all, so we will be replacing those values with the median. This might not be an ideal approach, but that's what we will go for now."
      ],
      "metadata": {
        "id": "C6bURuJhxa-p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# only keep non-zero values for RestingBP\n",
        "df_clean = df.copy()[df[\"RestingBP\"] != 0]"
      ],
      "metadata": {
        "id": "k8xXufb5zFCo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cleaner(x):\n",
        "    if round(x,1)==0:\n",
        "      x= df_clean[\"Cholesterol\"].median()\n",
        "    else:\n",
        "      pass\n",
        "    return x"
      ],
      "metadata": {
        "id": "AdZS_xBQ6mKk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cholesterol_clean = df_clean[\"Cholesterol\"].apply(cleaner)\n",
        "df[\"Cholesterol\"]= cholesterol_clean\n"
      ],
      "metadata": {
        "id": "aElZ3rzvzmHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean[[\"Cholesterol\", \"RestingBP\"]].describe()"
      ],
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        "colab": {
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        "outputId": "a4308c69-166a-4ed2-ff86-f6fac9680f1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "       Cholesterol   RestingBP\n",
              "count   917.000000  917.000000\n",
              "mean    199.016358  132.540894\n",
              "std     109.246330   17.999749\n",
              "min       0.000000   80.000000\n",
              "25%     174.000000  120.000000\n",
              "50%     223.000000  130.000000\n",
              "75%     267.000000  140.000000\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The minimum values for both have changed! \n",
        "There are no more zero values in either of those. \n"
      ],
      "metadata": {
        "id": "0WNVsGAi8bu-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feauture Selection\n",
        "Now that we've cleaned our data, we can now start selecting features for our model."
      ],
      "metadata": {
        "id": "xk-D_Mgg85_B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thanks to our EDA and a general understanding of the features, we can identify some of the features that we could start with:\n",
        "\n",
        "* Age\n",
        "* Sex\n",
        "* ChestPainType\n",
        "* Cholesterol\n",
        "* FastingBS\n",
        "\n",
        "In order to do that, we will first convert our categorical columns into dummy variables and also identify how strongly the feature columns are correlated to the target colummn. That should help us narrow down on the features."
      ],
      "metadata": {
        "id": "wn4lnADL8_Az"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean = pd.get_dummies(df_clean, drop_first=True)\n",
        "df_clean.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "HQ8fK6fm_dWB",
        "outputId": "f8fd336d-b3cf-4421-84cb-56cd5a7f9d99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
              "0   40        140          289          0    172      0.0             0   \n",
              "1   49        160          180          0    156      1.0             1   \n",
              "2   37        130          283          0     98      0.0             0   \n",
              "3   48        138          214          0    108      1.5             1   \n",
              "4   54        150          195          0    122      0.0             0   \n",
              "\n",
              "   Sex_M  ChestPainType_ATA  ChestPainType_NAP  ChestPainType_TA  \\\n",
              "0      1                  1                  0                 0   \n",
              "1      0                  0                  1                 0   \n",
              "2      1                  1                  0                 0   \n",
              "3      0                  0                  0                 0   \n",
              "4      1                  0                  1                 0   \n",
              "\n",
              "   RestingECG_Normal  RestingECG_ST  ExerciseAngina_Y  ST_Slope_Flat  \\\n",
              "0                  1              0                 0              0   \n",
              "1                  1              0                 0              1   \n",
              "2                  0              1                 0              0   \n",
              "3                  1              0                 1              1   \n",
              "4                  1              0                 0              0   \n",
              "\n",
              "   ST_Slope_Up  \n",
              "0            1  \n",
              "1            0  \n",
              "2            1  \n",
              "3            0  \n",
              "4            1  "
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              "                                                     [key], {});\n",
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        "correlations = abs(df_clean.corr())\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(correlations[correlations > 0.3], annot=True, cmap=\"Purples\")"
      ],
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "<Axes: >"
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          "metadata": {},
          "execution_count": 13
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our correlation heatmap, we can identify the following features to be positively correlated (correlation coefficient greater than 0.3) to HeartDisease:\n",
        "\n",
        "* `Oldpeak`\n",
        "* `MaxHR`\n",
        "* `ChestPainType_ATA`\n",
        "* `ExerciseAngina_Y`\n",
        "* `ST_Slope_Flat`\n",
        "* `ST_Slope_Up`\n",
        "\n",
        "The correlation between cholesterol and heart disease is weaker than anticipated, so we may want to consider excluding this feature temporarily. \n",
        "Taking into account our previous endeavors, we can narrow down our features to the following:\n",
        "\n",
        "* `Oldpeak`\n",
        "* `Sex_M`\n",
        "* `ExerciseAngina_Y`\n",
        "* `ST_Slope_Flat`\n",
        "* `ST_Slope_Up`\n",
        "\n",
        "Time to create our model using these features!"
      ],
      "metadata": {
        "id": "MSXJUjQEAAJR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building a Classifier with One Feature"
      ],
      "metadata": {
        "id": "w81mqajNDdcB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will split our dataset into a training and a test set first."
      ],
      "metadata": {
        "id": "6WFH1PGkE5A-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_clean.drop([\"HeartDisease\"], axis=1)\n",
        "y = df_clean[\"HeartDisease\"]\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.15, random_state = 417)\n",
        "\n",
        "features = [\n",
        "    \"Oldpeak\",\n",
        "    \"Sex_M\",\n",
        "    \"ExerciseAngina_Y\",\n",
        "    \"ST_Slope_Flat\",\n",
        "    \"ST_Slope_Up\"\n",
        "]"
      ],
      "metadata": {
        "id": "lG49X4dYE8o4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our initial step will be to generate a model for each of the features above and assess their performance using accuracy as a measure."
      ],
      "metadata": {
        "id": "wkCdUvyLFINu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in features:\n",
        "    knn = KNeighborsClassifier(n_neighbors = 3)\n",
        "    knn.fit(X_train[[feature]], y_train)\n",
        "    accuracy = knn.score(X_val[[feature]], y_val)\n",
        "    print(f\"The k-NN classifier trained on {feature} and with k = 3 has an accuracy of {accuracy*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjERCQoCFhfR",
        "outputId": "6a90f7ad-ee2e-4c1f-c467-ea32128314df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The k-NN classifier trained on Oldpeak and with k = 3 has an accuracy of 76.81%\n",
            "The k-NN classifier trained on Sex_M and with k = 3 has an accuracy of 44.93%\n",
            "The k-NN classifier trained on ExerciseAngina_Y and with k = 3 has an accuracy of 73.19%\n",
            "The k-NN classifier trained on ST_Slope_Flat and with k = 3 has an accuracy of 81.88%\n",
            "The k-NN classifier trained on ST_Slope_Up and with k = 3 has an accuracy of 84.06%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model that performed the best, achieving an accuracy of approximately 82%, was trained on the `ST_Slope_Flat` feature, with `ExerciseAngina_Y` coming in a close second. These choices align with the observed data distributions.\n",
        "\n",
        "As the next step, we'll develop a model using all of these features."
      ],
      "metadata": {
        "id": "tqPlyViFF9UD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building a Classifier with Multiple Features"
      ],
      "metadata": {
        "id": "QC8RFKAXGvl_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to train on all the features mentioned earlier, it's necessary to normalize the data first. To achieve this, we'll be utilizing scikit-learn's `MinMaxScaler` to rescale the values between 0 and 1. Afterwards, we'll train the model."
      ],
      "metadata": {
        "id": "FrmTaUtyG0Kz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train[features])\n",
        "X_val_scaled = scaler.fit_transform(X_val[features])"
      ],
      "metadata": {
        "id": "_cFFRawJG8d9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn.fit(X_train_scaled, y_train)\n",
        "accuracy = knn.score(X_val_scaled, y_val)\n",
        "print(f\"Accuracy: {accuracy*100:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2o_Bte-dHMX6",
        "outputId": "08acb4fd-6c6b-4d8b-c362-7c082aadf077"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 68.84\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although not an improvement, the accuracy of the model reduced to around 69%. It's not ideal, but it's a promising start.<br> Next, we'll investigate which parameters/hyperparameters may be optimal."
      ],
      "metadata": {
        "id": "nQuV5sELIFDi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "YYa0ivuOIo1d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To explore the optimal hyperparameters, we'll employ grid search. The following range of values will be examined for a couple of hyperparameters:\n",
        "\n",
        "* For k (n_neighbors in scikit-learn), values from 1 to 20 will be considered.\n",
        "* We'll be using two distance metrics - minkowski and manhattan."
      ],
      "metadata": {
        "id": "EOxoZnSDJcFH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train[features])\n",
        "X_test_scaled = scaler.fit_transform(X_val[features])\n",
        "y_test = y_val\n",
        "\n",
        "grid_params = {\"n_neighbors\": range(1, 20),\n",
        "                \"metric\": [\"minkowski\", \"manhattan\"],\n",
        "              }\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "knn_grid = GridSearchCV(knn, grid_params, scoring= 'accuracy' )\n",
        "knn_grid.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "kpI93ePyJwzb",
        "outputId": "a4fb2a9e-0288-4606-9e65-37a0591298c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={'metric': ['minkowski', 'manhattan'],\n",
              "                         'n_neighbors': range(1, 20)},\n",
              "             scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;metric&#x27;: [&#x27;minkowski&#x27;, &#x27;manhattan&#x27;],\n",
              "                         &#x27;n_neighbors&#x27;: range(1, 20)},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;metric&#x27;: [&#x27;minkowski&#x27;, &#x27;manhattan&#x27;],\n",
              "                         &#x27;n_neighbors&#x27;: range(1, 20)},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid.best_score_*100, knn_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYKl6eNoLIvR",
        "outputId": "d8abb0df-a833-4aef-e86f-010c49145941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(83.56327543424318, {'metric': 'minkowski', 'n_neighbors': 19})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our top-performing model attained an accuracy of around 83% utilizing 19 n_neighbors and minkowski as the distance metric. The accuracy of this model has significantly improved. \n",
        "\n",
        "We will evaluate our model on our test set now."
      ],
      "metadata": {
        "id": "eJO7qn7ZSjRM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation on Test Set"
      ],
      "metadata": {
        "id": "VKs2r5pHTL5z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = knn_grid.best_estimator_.score(X_test_scaled, y_test)\n",
        "print(f\" Model Accuracy on test set: {accuracy*100:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRscRFCrV0ON",
        "outputId": "a3085920-1a3a-47e8-dfc3-ccf355a79e89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Model Accuracy on test set: 85.51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model achieved an accuracy of approximately 86%, which is fantastic! <br> This implies that the model is highly likely to accurately predict whether a patient is at risk of developing heart disease around 86% of the time."
      ],
      "metadata": {
        "id": "XhgfvYiHWdAY"
      }
    }
  ]
}