Data_Preprocessing.ipynb

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      "name": "Data Preprocessing.ipynb",
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      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Ahmed-Ashraf-Marzouk/data-mining-algorithms/blob/main/Data_Preprocessing.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": [
        "# Imports"
      ],
      "metadata": {
        "id": "WseG4hpmdcKo"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ibFn_A5vmumg"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sn\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading the Data"
      ],
      "metadata": {
        "id": "qoQJ25p8df0z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown \"1uDSWhhUBPFjjUJmsh4-FLiRM2AakrC8f\"\n",
        "!unrar x \"./dataset.rar\" -idq"
      ],
      "metadata": {
        "id": "4DWcWnqeY0n6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6339f053-8f76-4a05-ad65-e99b5d0506ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uDSWhhUBPFjjUJmsh4-FLiRM2AakrC8f\n",
            "To: /content/dataset.rar\n",
            "\r  0% 0.00/3.82k [00:00<?, ?B/s]\r100% 3.82k/3.82k [00:00<00:00, 5.53MB/s]\n",
            "\n",
            "\n",
            "Would you like to replace the existing file dataset/heart.csv\n",
            " 11323 bytes, modified on 2021-03-22 09:41\n",
            "with a new one\n",
            " 11323 bytes, modified on 2021-03-22 09:41\n",
            "\n",
            "[Y]es, [N]o, [A]ll, n[E]ver, [R]ename, [Q]uit "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Wrangling"
      ],
      "metadata": {
        "id": "cJ4sBqCvd19R"
      }
    },
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      "cell_type": "markdown",
      "source": [
        "## Column Description\n",
        "1. **age** : Age of the patient\n",
        "2. **Gender** : Sex of the patient\n",
        "3. **exang**: exercise induced angina (1 = yes; 0 = no)\n",
        "4. **ca**: number of major vessels (0-3)\n",
        "5. **cp** : Chest Pain type chest pain type\n",
        "    - Value 1: typical angina\n",
        "    - Value 2: atypical angina\n",
        "    - Value 3: non-anginal pain\n",
        "    - Value 4: asymptomatic\n",
        "\n",
        "6. **trtbps** : resting blood pressure (in mm Hg)\n",
        "7. **chol** : cholestoral in mg/dl fetched via BMI sensor\n",
        "8. **fbs** : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false\n",
        "9. **rest_ecg** : resting electrocardiographic results\n",
        "    - Value 0: normal\n",
        "    - Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
        "    - Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
        "\n",
        "10. **thalach** : maximum heart rate achieved\n",
        "\n",
        "\n",
        "11. **oldpeak** : Previous peak\n",
        "\n",
        "12. **slp** : Slope\n",
        "\n",
        "13. **thall** : Thal rate\n",
        "\n",
        "14. **Output** : 0= less chance of heart attack 1= more chance of heart attack"
      ],
      "metadata": {
        "id": "UYdrvMYScdFY"
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    {
      "cell_type": "code",
      "source": [
        "col_names = ['Age','Gender','CP', 'Trtbps', 'Chol', 'FBS', 'Rest_ecg', 'Thalach', 'Exang', 'Old_peak', 'Slope', 'CA', 'Thall', 'Output']\n",
        "df = pd.read_csv('dataset/heart.csv', header = 0, names = col_names)\n",
        "rearranged_col_names = ['Age', 'Gender', 'Exang', 'CA', 'CP', 'Trtbps', 'Chol', 'FBS', 'Rest_ecg', 'Thalach', 'Old_peak', 'Slope','Thall', 'Output']\n",
        "df = df[rearranged_col_names]\n",
        "df.head()"
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              "   Age  Gender  Exang  CA  CP  Trtbps  Chol  FBS  Rest_ecg  Thalach  Old_peak  \\\n",
              "0   63       1      0   0   3     145   233    1         0      150       2.3   \n",
              "1   37       1      0   0   2     130   250    0         1      187       3.5   \n",
              "2   41       0      0   0   1     130   204    0         0      172       1.4   \n",
              "3   56       1      0   0   1     120   236    0         1      178       0.8   \n",
              "4   57       0      1   0   0     120   354    0         1      163       0.6   \n",
              "\n",
              "   Slope  Thall  Output  \n",
              "0      0      1       1  \n",
              "1      0      2       1  \n",
              "2      2      2       1  \n",
              "3      2      2       1  \n",
              "4      2      2       1  "
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        "\n",
        "\n",
        "---\n",
        "\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## Checking the Data"
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        "df.info() # No null values in dataset\n",
        "# 303 Examples\n",
        "# 14 Features\n",
        "# (303) X (14)\n"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Age       303 non-null    int64  \n",
            " 1   Gender    303 non-null    int64  \n",
            " 2   Exang     303 non-null    int64  \n",
            " 3   CA        303 non-null    int64  \n",
            " 4   CP        303 non-null    int64  \n",
            " 5   Trtbps    303 non-null    int64  \n",
            " 6   Chol      303 non-null    int64  \n",
            " 7   FBS       303 non-null    int64  \n",
            " 8   Rest_ecg  303 non-null    int64  \n",
            " 9   Thalach   303 non-null    int64  \n",
            " 10  Old_peak  303 non-null    float64\n",
            " 11  Slope     303 non-null    int64  \n",
            " 12  Thall     303 non-null    int64  \n",
            " 13  Output    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
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        "df.describe()\n",
        "# Gender range between 29 and 77 which is acceptable range\n",
        "# Number of major vessales (column name CA) should be between 0 and 3, but 4 can be found.\n",
        "# Resting blood pressure (column name Trtbps) have a maximum value of 200 mm Hg, is this possible?\n",
        "# Maximum heart rate achieved (column name Thalach) have a maximum value of 202 units, is this possible?"
      ],
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              "              Age      Gender       Exang          CA          CP      Trtbps  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.326733    0.729373    0.966997  131.623762   \n",
              "std      9.082101    0.466011    0.469794    1.022606    1.032052   17.538143   \n",
              "min     29.000000    0.000000    0.000000    0.000000    0.000000   94.000000   \n",
              "25%     47.500000    0.000000    0.000000    0.000000    0.000000  120.000000   \n",
              "50%     55.000000    1.000000    0.000000    0.000000    1.000000  130.000000   \n",
              "75%     61.000000    1.000000    1.000000    1.000000    2.000000  140.000000   \n",
              "max     77.000000    1.000000    1.000000    4.000000    3.000000  200.000000   \n",
              "\n",
              "             Chol         FBS    Rest_ecg     Thalach    Old_peak       Slope  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean   246.264026    0.148515    0.528053  149.646865    1.039604    1.399340   \n",
              "std     51.830751    0.356198    0.525860   22.905161    1.161075    0.616226   \n",
              "min    126.000000    0.000000    0.000000   71.000000    0.000000    0.000000   \n",
              "25%    211.000000    0.000000    0.000000  133.500000    0.000000    1.000000   \n",
              "50%    240.000000    0.000000    1.000000  153.000000    0.800000    1.000000   \n",
              "75%    274.500000    0.000000    1.000000  166.000000    1.600000    2.000000   \n",
              "max    564.000000    1.000000    2.000000  202.000000    6.200000    2.000000   \n",
              "\n",
              "            Thall      Output  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
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              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dd1f95d-1621-4572-8b78-df7c29377479')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8dd1f95d-1621-4572-8b78-df7c29377479 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-8dd1f95d-1621-4572-8b78-df7c29377479');\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": "code",
      "source": [
        "df['Output'].value_counts(normalize=True) # Each class is about 50%"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqTCuCLgbnoJ",
        "outputId": "f7cd9e81-4d7e-4d98-855a-7edc4c17089e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.544554\n",
              "0    0.455446\n",
              "Name: Output, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data visualization and clearning"
      ],
      "metadata": {
        "id": "Pa0BAaqeYxtD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outlier detection and removal"
      ],
      "metadata": {
        "id": "c7h8NrmxSgu4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# selected columns before removing outliers\n",
        "arr = [df['Chol'].to_numpy(), df['Thalach'].to_numpy(), df['Trtbps'].to_numpy()]\n",
        "plt.boxplot(arr)\n",
        "plt.xticks([1, 2, 3], ['Chol', 'Thalach', 'Trtbps'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bK84QEaeST2C",
        "outputId": "b61a35af-2a31-49ee-8f6f-c9dc3421c5cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleaned data after removing outliers\n",
        "df = df.loc[df['Chol'] < 380]\n",
        "df = df.loc[df['Thalach'] > 80]\n",
        "df = df.loc[df['Trtbps'] < 170]\n",
        "arr = [df['Chol'].to_numpy(), df['Thalach'].to_numpy(), df['Trtbps'].to_numpy()]\n",
        "plt.boxplot(arr)\n",
        "plt.xticks([1, 2, 3], ['Chol', 'Thalach', 'Trtbps'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "c0TSEIAED1mj",
        "outputId": "cde3d49f-b00e-43d7-bea5-266326af3e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# selected columns before removing outliers \n",
        "arr = [df['CA'].to_numpy(), df['FBS'].to_numpy(), df['Old_peak'].to_numpy(), df['Thall'].to_numpy()]\n",
        "plt.boxplot(arr)\n",
        "plt.xticks([1, 2, 3, 4], ['CA', 'FBS', 'Old_peak', 'Thall'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "ZsblCKicSI9P",
        "outputId": "79328fad-9f7b-4b5c-81f9-75ccff496107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleaned data after removing outliers\n",
        "df = df.loc[df['CA'] < 2.5]\n",
        "df = df.loc[df['FBS'] < 0.5]\n",
        "df = df.loc[df['Old_peak'] < 3.5]\n",
        "df = df.loc[df['Thall'] > 1]\n",
        "arr = [df['CA'].to_numpy(), df['FBS'].to_numpy(), df['Old_peak'].to_numpy(), df['Thall'].to_numpy()]\n",
        "plt.boxplot(arr)\n",
        "plt.xticks([1, 2, 3, 4], ['CA', 'FBS', 'Old_peak', 'Thall'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "zl2zHEcpGYMf",
        "outputId": "279fad4d-cbbd-43a3-fbea-6f9de5327e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kedplot(df['T'])"
      ],
      "metadata": {
        "id": "8mQsH6Qcakg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation matrix"
      ],
      "metadata": {
        "id": "yKIm1D-sZ5TV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation matrix helps us to detect correlated features in our data set. So, we can remove strongly correlated data with small effect on our final machine learning model. "
      ],
      "metadata": {
        "id": "2Yaneu0vg6rM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corrMatrix = df.corr()\n",
        "plt.figure(figsize=(20,5))\n",
        "sn.heatmap(corrMatrix, annot=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "9Se8C0vQZ43I",
        "outputId": "f678f1cd-71d0-43a5-8188-9ec2fe9901e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcec7310cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation between age and heart rate"
      ],
      "metadata": {
        "id": "efBmcgrwZzIE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# scatter plot between $age and $heart_rate\n",
        "age_x = df['Age']\n",
        "heart_rate_y = df['Thalach'] \n",
        "plt.scatter(age_x, heart_rate_y)\n",
        "plt.xlabel('age')\n",
        "plt.ylabel('Thalach')\n",
        "plt.title('Age vs Heart rate')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "# From graph below we can see that age is inversely correlated with heart rate. When age increases heart rate decreases."
      ],
      "metadata": {
        "id": "62N2fDrlpIJM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "34449904-5233-420a-b51c-f1b3fdb11aa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assume that 0: female and 1: male\n",
        "count = df['Gender'].value_counts()\n",
        "print(count)\n",
        "plt.bar(['male', 'female'], count, color=['#293462', '#F24C4C'])\n",
        "plt.show()\n",
        "# according to the graph our data has more men than women, which may lead to bias towards men in our machine learning detection model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "YkPfEJfth455",
        "outputId": "21b4184e-c417-4459-d848-1b760cbfed7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    134\n",
            "0     73\n",
            "Name: Gender, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the Cleaned Data Frame"
      ],
      "metadata": {
        "id": "omw2tjy1g7EL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from os.path import exists\n",
        "\n",
        "##############################################################################################################\n",
        "##                                                                                                          ##\n",
        "##      Ignore this, Change file_path, file_name, and df_to_save then run the cell and wait for output.     ##\n",
        "def save_df(local_path, file_name, df_to_save):\n",
        "    overwrite = True\n",
        "\n",
        "    if not(exists('/content/' + local_path)):\n",
        "        overwrite = False\n",
        "        print(f\"\\n!! Saving failed, path to folder does not exist, {'/content/' + local_path}\")\n",
        "\n",
        "    if(exists(local_path+file_name)):\n",
        "        try:\n",
        "            overwrite = int(input('This file already exists!\\n\\nDo you wish to overwrite it? [1]Yes, [0]No:  '))\n",
        "        except:\n",
        "            overwrite = False\n",
        "            print(\"\\n!! Saving failed, please enter either 1 or 0.\")\n",
        "\n",
        "\n",
        "\n",
        "    if(overwrite):\n",
        "        print(f\"\\n\\nFile Saved to {'/content/' + local_path + file_name}\")\n",
        "        df_to_save.to_csv('/content/' + local_path + file_name)\n",
        "    else:\n",
        "        print('\\n\\nNo Changes')\n",
        "##                                                                                                          ##\n",
        "##############################################################################################################\n",
        "\n",
        "local_path = 'Clean DataFrame/' # must end with '/'\n",
        "# this folder must exists with the Google Colab 'content' folder (the default one)\n",
        "# if it does not, then create it manualy.\n",
        "\n",
        "file_name = 'cleaned_data.csv'\n",
        "# the name of the csv file\n",
        "\n",
        "df_to_save = df.copy()\n",
        "# The name of the df to save\n",
        "\n",
        "save_df(local_path, file_name, df_to_save) # Check output.\n",
        "# download locally if needed\n"
      ],
      "metadata": {
        "id": "KsvgjLRnZsBb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4898d84b-298f-4a54-92df-df04cdb25ef9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "File Saved to /content/Clean DataFrame/cleaned_data.csv\n"
          ]
        }
      ]
    }
  ]
}