FDS project code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxBiiISYSywj"
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      "source": [
        "# Connect to drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6yw29yS0jEi"
      },
      "source": [
        "To begin we will connect to Google Drive, which will be useful later to upload the dataset we already have in our Google Drive corresponding folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "id": "_83Y69I1SRtp",
        "outputId": "38701c0d-24d8-4811-a6d1-1d6640fd84ec"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knYFhChjTTrp"
      },
      "source": [
        "# Libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0msfKKUK1sr_"
      },
      "source": [
        "After uploading the dataset we are going to import some libraries that will come in handy for the different phases of our analysis:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-be2b7907542b>:5: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use('seaborn-whitegrid')\n"
          ]
        }
      ],
      "source": [
        "#visualization libraries\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "import seaborn as sns #based on matplotlib\n",
        "import os\n",
        "\n",
        "#for basic manipulation\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#for machine-learning (scikit-learn)\n",
        "from sklearn.preprocessing import StandardScaler, normalize\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_squared_error\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MREmB0ktTh2w"
      },
      "source": [
        "# Pre-processing of the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxnEyx0D-EPU"
      },
      "source": [
        "Now we can start the pre-processing, that involves transforming raw structured data into a structured format that is more suitable for subsequent analysis and modelling:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "DkMsVyHKUOKw"
      },
      "outputs": [],
      "source": [
        "#Get the dataset from Google Drive\n",
        "dataset = pd.read_csv(\"/content/drive/MyDrive/DATA SCIENCE 23 24/FDS_Final_Project/healthcare-dataset-stroke-data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWgT80YxdlIw",
        "outputId": "8d3953cf-9090-4b32-a6da-5cfcf303466a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Inspect the dataset\n",
        "dataset.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTOXyQVDQnXZ"
      },
      "source": [
        "Since we already have a small dataset, 5110 rows, deleting 201 Na's isn't the smartest choice. Therefore we decide to fill the missing values with the mean of the avaliable values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "fFOy2GJmVmJG"
      },
      "outputs": [],
      "source": [
        "#Replaces the missing values in the 'bmi' column with the mean of the available values\n",
        "dataset.bmi.fillna(dataset.bmi.mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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        "outputId": "54e57b77-044d-4d21-ef1f-61c9b0359746"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "gender               0\n",
              "age                  0\n",
              "hypertension         0\n",
              "heart_disease        0\n",
              "ever_married         0\n",
              "work_type            0\n",
              "Residence_type       0\n",
              "avg_glucose_level    0\n",
              "bmi                  0\n",
              "smoking_status       0\n",
              "stroke               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#Let's check if the compiled succesfully\n",
        "dataset.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nb93RhxWRD7h"
      },
      "source": [
        "> We can see that there are no missing values anymore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCC5vbBGR8j3"
      },
      "source": [
        "Now let's have a look at the unique values of each variable of the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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              "                                                                   0\n",
              "id                 [9046, 51676, 31112, 60182, 1665, 56669, 53882...\n",
              "gender                                         [Male, Female, Other]\n",
              "age                [67.0, 61.0, 80.0, 49.0, 79.0, 81.0, 74.0, 69....\n",
              "hypertension                                                  [0, 1]\n",
              "heart_disease                                                 [1, 0]\n",
              "ever_married                                               [Yes, No]\n",
              "work_type          [Private, Self-employed, Govt_job, children, N...\n",
              "Residence_type                                        [Urban, Rural]\n",
              "avg_glucose_level  [228.69, 202.21, 105.92, 171.23, 174.12, 186.2...\n",
              "bmi                [36.6, 28.893236911794666, 32.5, 34.4, 24.0, 2...\n",
              "smoking_status      [formerly smoked, never smoked, smokes, Unknown]\n",
              "stroke                                                        [1, 0]"
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          "metadata": {},
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      "source": [
        "#Get the unique values that will be outputed as a series object\n",
        "unique_values = pd.Series({col: dataset[col].unique() for col in dataset.columns})\n",
        "\n",
        "#convert the series into a dataframe\n",
        "unique_values_df = unique_values.to_frame()\n",
        "unique_values_df"
      ]
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    {
      "cell_type": "markdown",
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        "id": "gvM9LEHWTDKz"
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      "source": [
        "Looking at the unique values we can start by checking the folllwing things:\n",
        "1. See if the percentage of \"Other\" in the \"gender\" column with respect to \"Male\" and \"Female\" is small enough to be deleted.\n",
        "\n",
        "2. Look for negative values in the \"age\", \"bmi\" and \"avg_glucose_level\" columns, that in case won't be acceptable levels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMLcqf4CUO5J"
      },
      "source": [
        "1) Percentage of \"Other\" with respect to \"Female\" and \"Male\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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          "name": "stdout",
          "text": [
            "We can see that we have just 1 'Other' gender value, while we have 2994 females and 2115 males.\n",
            "The 'Other' value can be deleted from the dataset.\n"
          ]
        }
      ],
      "source": [
        "n_Others =sum(dataset['gender'] =='Other')\n",
        "n_Female =sum(dataset['gender'] =='Female')\n",
        "n_Male =sum(dataset['gender'] =='Male')\n",
        "\n",
        "print(\"We can see that we have just \" + str(n_Others) + \" 'Other' gender value, while we have \" + str(n_Female) + \" females and \" + str(n_Male) + \" males.\\nThe 'Other' value can be deleted from the dataset.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "m0a0DOU0dpDR"
      },
      "outputs": [],
      "source": [
        "# It removes the single row where the 'gender' column has the value 'Other'\n",
        "dataset.drop(dataset.loc[dataset['gender'] =='Other'].index, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qULzew_-VfI0"
      },
      "source": [
        "2)  Let's see the potential negative values of \"age\", \"bmi\" and \"avg_glucose_level\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7beuC3EX9iE",
        "outputId": "b90a1729-fd29-4c1a-a9a8-36c6c70ee564"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Check if there are negative values in the 'age', 'bmi', and 'avg_glucose_level' columns\n",
        "negative_values = dataset[(dataset['age'] < 0) | (dataset['bmi'] < 0) | (dataset['avg_glucose_level'] < 0)]\n",
        "len(negative_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7gBTRTUY80F"
      },
      "source": [
        "> From the obtained result we can see that there are no negative values for the selected  columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4TpMiOOAfd1"
      },
      "source": [
        "Now that we had a more in-depth look at our dataset we can move forward and start a descriptive statistical analysis to have a better understanding of our variables and our dataset in general."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JVFc9xBUcd2"
      },
      "source": [
        "# Descriptive statistical analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeQkOqDz_Tru"
      },
      "source": [
        "> Let's get the dataset informations first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ah8Wn_bSUjxp",
        "outputId": "98266193-4a15-4141-b0e8-63285a805fc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5109 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5109 non-null   int64  \n",
            " 1   gender             5109 non-null   object \n",
            " 2   age                5109 non-null   float64\n",
            " 3   hypertension       5109 non-null   int64  \n",
            " 4   heart_disease      5109 non-null   int64  \n",
            " 5   ever_married       5109 non-null   object \n",
            " 6   work_type          5109 non-null   object \n",
            " 7   Residence_type     5109 non-null   object \n",
            " 8   avg_glucose_level  5109 non-null   float64\n",
            " 9   bmi                5109 non-null   float64\n",
            " 10  smoking_status     5109 non-null   object \n",
            " 11  stroke             5109 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 518.9+ KB\n"
          ]
        }
      ],
      "source": [
        "dataset.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2rICMDJ_cmd"
      },
      "source": [
        "> And now let's see better what kind of variables we have in our dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zFvI2utiie7l",
        "outputId": "d93bcfc0-b2a4-45f9-a33f-a7e1dfa2b647"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0   9046    Male  67.0             0              1          Yes   \n",
              "1  51676  Female  61.0             0              0          Yes   \n",
              "2  31112    Male  80.0             0              1          Yes   \n",
              "3  60182  Female  49.0             0              0          Yes   \n",
              "4   1665  Female  79.0             1              0          Yes   \n",
              "\n",
              "       work_type Residence_type  avg_glucose_level        bmi  \\\n",
              "0        Private          Urban             228.69  36.600000   \n",
              "1  Self-employed          Rural             202.21  28.893237   \n",
              "2        Private          Rural             105.92  32.500000   \n",
              "3        Private          Urban             171.23  34.400000   \n",
              "4  Self-employed          Rural             174.12  24.000000   \n",
              "\n",
              "    smoking_status  stroke  \n",
              "0  formerly smoked       1  \n",
              "1     never smoked       1  \n",
              "2     never smoked       1  \n",
              "3           smokes       1  \n",
              "4     never smoked       1  "
            ],
            "text/html": [
              "\n",
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              "      <th>id</th>\n",
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              "      <td>49.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.400000</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>79.0</td>\n",
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              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-45bf9ebb-6127-432b-b7ce-0ac6b34ad220 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-45bf9ebb-6127-432b-b7ce-0ac6b34ad220');\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b7b45f94-0dec-492f-b64a-75eea2dfb17a')\"\n",
              "            title=\"Suggest charts\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b7b45f94-0dec-492f-b64a-75eea2dfb17a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSqn2Bdb_iHQ"
      },
      "source": [
        "# **Plots**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJYA8aytWxl4"
      },
      "source": [
        "> ### ***Boxplot***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xugnzckBBG1I"
      },
      "source": [
        "After analyzing the types of variables in our dataset, we decided to use a boxplot to visualize the distribution of the numerical variables, because these type of graphs are an effective tool for summarizing the distribution of the data. They also provide some statistical indicators, like the quartiles, the median, and the whiskers that extend from the box to the minimum and maximum data points. \\\\\n",
        "Boxplots can also help identify outliers, which are data points that significantly differ from other observations.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "CwUQvJXj1RVF",
        "outputId": "81b77bd3-d14e-4eb4-b92f-48ac33cb69ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Boxplot for BMI (Body Mass Index)\n",
        "sns.boxplot(data=dataset['bmi'], ax=axs[0])\n",
        "axs[0].set_title('BMI Boxplot')\n",
        "axs[0].axhline(30, color=(0.75, 0.5, 0.25), linestyle='--')\n",
        "axs[0].text(0.6, 30, '30', ha='center', va='bottom', color=(0.75, 0.5, 0.25))\n",
        "axs[0].axhline(35, color='orange', linestyle='--')\n",
        "axs[0].text(0.6, 35, '35', ha='center', va='bottom', color='orange')\n",
        "axs[0].axhline(40, color='darkred', linestyle='--')\n",
        "axs[0].text(0.6, 40, '40', ha='center', va='bottom', color='darkred')\n",
        "\n",
        "# Boxplot for average glucose level\n",
        "sns.boxplot(data=dataset['avg_glucose_level'], ax=axs[1], color = \"orange\")\n",
        "axs[1].set_title('Glucose Boxplot')\n",
        "axs[1].axhline(126, color='green', linestyle='--')\n",
        "axs[1].text(0.6, 126, '126', ha='center', va='bottom', color='green')\n",
        "\n",
        "# Create legend handles\n",
        "red_patch = mpatches.Patch(color='darkred', label='Obesity class III')\n",
        "light_brown_patch = mpatches.Patch(color=(0.75, 0.5, 0.25), label='Obesity class I')\n",
        "orange_patch = mpatches.Patch(color='orange', label='Obesity class II')\n",
        "green_patch = mpatches.Patch(color='green', label='Diabetes')\n",
        "\n",
        "# Add a legend\n",
        "axs[0].legend(handles=[red_patch, orange_patch, light_brown_patch], loc='upper right')\n",
        "axs[1].legend(handles=[green_patch], loc='upper right')\n",
        "\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfqS0FG_A04E"
      },
      "source": [
        "We notice a high number of outliers in both distributions. To interpret these boxplots accurately, it's essential to understand the relationship between glucose levels and diabetes. Diabetes can often be diagnosed through blood glucose measurements.\n",
        "\n",
        "Let's consider these standard values:\n",
        "\n",
        "- Venous blood glucose concentration: 3.9 – 5.5 mmol/l (70-100 mg/dl)\n",
        "- Capillary blood glucose concentration: 4.4 – 6.1 mmol/l (80-110 mg/dl)\n",
        "- Plasma blood glucose concentration: 4.2 – 6.4 mmol/l (75-115 mg/dl)\n",
        "- Blood glucose level value for diabetes diagnosis: 7.8 mmol/l (140 mg/dl)\n",
        "\n",
        "The diagnosis of diabetes mellitus occurs when blood glucose levels reach or exceed 126 mg/dl. [[1]](https://www.laleggepertutti.it/260422_diabete-i-valori-di-riferimento-della-glicemia)\n",
        "\n",
        "These values provide insight into the outliers in the average glucose level distribution. Most likely, these individuals may have diabetes or are at risk of developing diabetes-related complications.\n",
        "\n",
        "Now, let's turn to the BMI boxplot where we can apply a similar reasoning. Given these BMI categories [[2]](https://www.salute.gov.it/portale/nutrizione/dettaglioIMCNutrizione.jsp?lingua=italiano&id=5479&area=nutrizione&menu=vuoto):\n",
        "\n",
        "- Underweight: 16.00 - 18.49\n",
        "- Normal weight: 18.50 - 24.99\n",
        "- Overweight: 25.00-29.99\n",
        "- Obesity class 1: 30.00-34.99\n",
        "- Obesity class 2: 35.00-39.99\n",
        "- Obesity class 3: ≥ 40.00\n",
        "\n",
        "The outliers in our population are likely those who are considered obese or overweight."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyuzUJNYWoY6"
      },
      "source": [
        "> ### ***Barplot***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOuE6aZUFHoi"
      },
      "source": [
        "Now we can plot the barplots for the categorical variables. \\\\\n",
        "Barplots are a good way to represent categorical data because they allow you to visualize the distribution of categorical variables by comparing the counts or frequencies of each category.\n",
        "\n",
        "In a barplot, each category is represented by a bar, and the length or height of the bar corresponds to the count or frequency of that category. This makes it easy to compare the counts or frequencies of different categories at a glance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KXyFEuye1biR",
        "outputId": "d37d8a00-6c76-48dd-eff5-cee87040badd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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hL7/8UnPnztXbb7+t4OBgLVq0SMOGDdPnn3+uOnXqaM6cObLb7UpJSVHt2rU1depUPf3001qwYMFFa/Dx8ZH084dVu3btqz5nAAAAVE5hYaH279/vzG2X4vZwezGrV69WkyZNNGDAAElSu3bt1LlzZy1btkwRERFKSUlR//79FRUVJennd8kvWrRI69evV/fu3bV8+XK9/PLLatSokSRp9OjR6tmzp44dO6aGDRtecL3zWxFq166tOnXq1NAsAQAAUFmX20Lq9nA7e/Zsff/998rLy9P999+vSZMmyW63KzQ01GVcaGio1q5dK0my2+3q0aOHs8/T01MhISFKT09XSEiIzp49q7CwMGd/06ZN5evrK7vdftFwe15ZWZnKysqqeYYAAAC4UhXNaG4Nt9HR0WrXrp1efvllHTp0SKNHj9aMGTPkcDguCKGBgYHKycmRJDkcDgUEBLj0BwQEKCcnRw6HQ5Lk7+/v0u/v7+88/lIyMjKucEYAAABwJ7eG25SUFOfPTZs21fjx4zV8+HC1bNnyssde7t0TVXk3hc1mY1sCAADANaigoKBCC5Fu35bwS7feeqvKysrk6enpXIE9LycnR0FBQZKkevXqXdDvcDjUrFkz5xiHw6G6des6+3Nzc3/1zjpJ8vLykpeX15VPBAAAANWqohnNbQ913bVrl1566SWXtszMTHl7eys2NlY7d+506du5c6fzBrLw8HDZ7XZnX1lZmXbt2qWoqCjddtttCggIcOnPyMhQSUmJ81FhAAAAsCa3hdv69esrJSVFycnJKikpUVZWll5//XU99NBDeuCBB5Sdna1ly5apuLhYqampSk1NVXx8vCQpMTFRK1asUFpamgoLCzV//nx5e3urU6dO8vLyUnx8vBYsWKAjR44oJydHr776qrp166YGDRq4a7oAAACoAR6mKptTq8m3336r2bNna+/evfL29la/fv00ZswY+fj46Ntvv1VSUpIyMzN1yy23aNy4cbr33nudx37wwQdKTk7WqVOnFBERoenTp8tms0mSSkpK9OKLL2rNmjUqLS1VXFycpk+fruuvv/6idRQUFGj37t0KCQlhzy0AAMA1qKJ5za3h9lpBuAUAALi2VTSvuW1bAgAAAFDdCLcAAACwDMItAAAALOOaes7tf6uWTy1ydwkArpKtMx92dwkA8F+FlVsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlnHNhNsXXnhBwcHBzt83b96sAQMGqEWLFurZs6dWrVrlMn7RokXq3r27WrRoocTERO3cudPZV1xcrGnTpqljx46KiYnRqFGjlJOTU2NzAQAAgHtcE+F29+7dWrlypfP348ePa8SIEUpISNDmzZs1efJkTZ06Venp6ZKkL7/8UnPnztUrr7yiTZs2KS4uTsOGDVNBQYEkac6cObLb7UpJSdFnn30mY4yefvppt8wNAAAANcft4ba8vFzPPvusBg8e7GxbvXq1mjRpogEDBsjHx0ft2rVT586dtWzZMklSSkqK+vfvr6ioKPn6+uqxxx6TJK1fv16lpaVavny5RowYoUaNGikwMFCjR4/Whg0bdOzYMXdMEQAAADWklrsLWLp0qXx8fNS7d2+99tprkiS73a7Q0FCXcaGhoVq7dq2zv0ePHs4+T09PhYSEKD09XSEhITp79qzCwsKc/U2bNpWvr6/sdrsaNmx4yVrKyspUVlZWjbMD8N+O7xQAqB4V/T51a7g9efKk5s6dq8WLF7u0OxyOC0JoYGCgc9+sw+FQQECAS39AQIBycnLkcDgkSf7+/i79/v7+l913m5GRUZVpAMAlpaWlubsEAPiv4tZw++KLL6p///6688479dNPP1XqWGPMFfVfjM1mU506dSp93BVbaq/5awKoEdHR0e4uAQAsoaCgoEILkW4Lt5s3b9b333+vjz/++IK+evXqOVdgz8vJyVFQUNAl+x0Oh5o1a+Yc43A4VLduXWd/bm6u6tev/6s1eXl5ycvLqwqzAYCL4zsFAKpHRb9P3XZD2apVq3Tq1CnFxcUpJiZG/fv3lyTFxMTIZrO5PNpLknbu3KmoqChJUnh4uOz2f692lpWVadeuXYqKitJtt92mgIAAl/6MjAyVlJQoPDy8BmYGAAAAd3FbuJ00aZI+++wzrVy5UitXrlRycrIkaeXKlerdu7eys7O1bNkyFRcXKzU1VampqYqPj5ckJSYmasWKFUpLS1NhYaHmz58vb29vderUSV5eXoqPj9eCBQt05MgR5eTk6NVXX1W3bt3UoEEDd00XAAAANcBt2xICAgJcbgorLS2VJN10002SpIULFyopKUkzZszQLbfcopkzZ+quu+6SJHXs2FFjx47V6NGjderUKUVERCg5OVm+vr6SpFGjRik/P18PPPCASktLFRcXp+nTp9fsBAEAAFDjPExV7ryymIKCAu3evVshISFuuaGs5VOLavyaAGrG1pkPu7sEALCEiuY1t7/EAQAAAKguhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYhlvD7Z49e/TII4+oZcuWateunUaPHq0TJ05IkjZv3qwBAwaoRYsW6tmzp1atWuVy7KJFi9S9e3e1aNFCiYmJ2rlzp7OvuLhY06ZNU8eOHRUTE6NRo0YpJyenRucGAACAmue2cFtSUqJHH31Ubdq00ebNm/Xxxx/r1KlTmj59uo4fP64RI0YoISFBmzdv1uTJkzV16lSlp6dLkr788kvNnTtXr7zyijZt2qS4uDgNGzZMBQUFkqQ5c+bIbrcrJSVFn332mYwxevrpp901VQAAANQQt4XbwsJCjRkzRkOHDpW3t7eCgoLUrVs3/fDDD1q9erWaNGmiAQMGyMfHR+3atVPnzp21bNkySVJKSor69++vqKgo+fr66rHHHpMkrV+/XqWlpVq+fLlGjBihRo0aKTAwUKNHj9aGDRt07Ngxd00XAAAANaCWuy4cEBCgBx980Pn7jz/+qI8++kj333+/7Ha7QkNDXcaHhoZq7dq1kiS73a4ePXo4+zw9PRUSEqL09HSFhITo7NmzCgsLc/Y3bdpUvr6+stvtatiw4SVrKisrU1lZWXVNEQD4TgGAalLR71O3hdvzsrOz1b17d5WWlio+Pl6jRo3S448/fkEIDQwMdO6bdTgcCggIcOkPCAhQTk6OHA6HJMnf39+l39/f/7L7bjMyMq5wNgDgKi0tzd0lAMB/FbeH21tuuUXp6ek6cOCApk2bpgkTJlToOGPMFfVfjM1mU506dSp93BVbaq/5awKoEdHR0e4uAQAsoaCgoEILkW4Pt5Lk4eGhJk2aaMyYMUpISFBsbKxzBfa8nJwcBQUFSZLq1at3Qb/D4VCzZs2cYxwOh+rWrevsz83NVf369X+1Di8vL3l5eV35hADg/+M7BQCqR0W/T912Q9nmzZvVvXt3lZeX/7sYz5/LiYyMdHm0lyTt3LlTUVFRkqTw8HDZ7f9e7SwrK9OuXbsUFRWl2267TQEBAS79GRkZKikpUXh4+NWcEgAAANzMbeE2PDxceXl5mjlzpgoLC3X69GnNnTtXrVq1UmJiorKzs7Vs2TIVFxcrNTVVqampio+PlyQlJiZqxYoVSktLU2FhoebPny9vb2916tRJXl5eio+P14IFC3TkyBHl5OTo1VdfVbdu3dSgQQN3TRcAAAA1wG3bEq6//nq98847SkpKUtu2bVWnTh21bdtWzz//vOrXr6+FCxcqKSlJM2bM0C233KKZM2fqrrvukiR17NhRY8eO1ejRo3Xq1ClFREQoOTlZvr6+kqRRo0YpPz9fDzzwgEpLSxUXF6fp06e7a6oAAACoIR6mKndeWUxBQYF2796tkJAQt9xQ1vKpRTV+TQA1Y+vMh91dAgBYQkXzmltfvwsAAABUJ8ItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwjGoPtzx8AQAAAO5SpXDbvXv3i7afOXNG7dq1u6KCAAAAgKqq1EscNm/erE2bNik7O1uvvvrqBf0//fSTSkpKqq04AAAAoDIqFW4DAgJUUFCgsrIyff/99xf0+/r6KikpqdqKAwAAACqjUuE2NDRUoaGh8vDw0JQpU65WTQAAAECVVCrcnjdlyhSlp6crMzNTxcXFF/Q/9NBDV1wYAAAAUFlVCrcvvPCCFi1apKCgIPn6+rr0eXh4EG4BAADgFlUKtytWrNC7776ru+++u7rrAQAAAKqsSo8C8/b2VqtWraq7FgAAAOCKVCncDh48WO+880511wIAAABckSptS9i2bZu2bdumxYsX6+abb5anp2tGXrp0abUUBwAAAFRGlcLt+UeCAQAAANeSKoXbkSNHVncdAAAAwBWrUridN2/er/YTfgEAAOAOVQq3X3/9tcvvZWVlys7OljFGLVq0qJbCAAAAgMqqUrhNSUm5oK28vFwLFiyQt7f3FRcFAAAAVEWVHgV20RN5eurxxx/nEWEAAABwm2oLt5L07bffqrS0tDpPCQAAAFRYlbYltG/f/oK2oqIi5efna/DgwVdaEwAAAFAlVQq348aNu6DNx8dHjRs3VlhY2BUXBQAAAFRFlcJtv379JEnnzp3T8ePH5eHhoYYNG8rLy6taiwMAAAAqo0rh9syZM3r22We1bt065x5bHx8f9erVS1OnTpWPj0+1FgkAAABURJXC7fTp03XixAnNmzdPjRs3liRlZmZqwYIFmjVrliZPnlytRQIAAAAVUeWXOHz22WcKCgpytjVp0kTh4eFKSEgg3AIAAMAtqvQoMC8vL9WuXfuCdn9/fxUUFFxxUQAAAEBVVCnctmjRQjNmzNDp06edbadPn9Zzzz2niIiIaisOAAAAqIwqbUt49tlnNWLECN1zzz3y9/eX9PNNZk2bNtWbb75ZrQUCAAAAFVWlcNuwYUONHz9etWvX1smTJ1VSUqIbbrhBRUVFuv3226u7RgAAAKBCqrQtYfHixXryySdVWFiorl27qkePHjpz5ozGjx+v999/v7prBAAAACqkSuH23Xff1fvvv6+7777b2dalSxctXrxY7777brUVBwAAAFRGlcJtTk6Ofve7313Qfuutt7rcZAYAAADUpCo/LeHVV1/V2bNnnW0nT57UCy+8oKioqGorDgAAAKiMKr+h7Mknn9SiRYvk5+en8vJy5efnKyQkRAsWLKjuGgEAAIAKqVK4ve2227RixQrt3r1bBw8elKenp2677Tbddddd1V0fAAAAUGFVCrfnhYSEKCQkpLpqAQAAAK5IlfbcAgAAANciwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMt4bb7Oxs/fGPf1RMTIzatWunSZMm6cyZM5Kk3bt3a+DAgWrZsqXuvfdevfPOOy7HfvLJJ+rdu7eaN2+u/v37a+PGjc6+8vJyzZkzR126dFHr1q01ZMgQHTp0qEbnBgAAgJrn1nA7bNgw+fv768svv9Tf//53/fDDD3r55ZdVVFSkoUOHqm3btvr66681Z84cLVy4UJ9//rmkn4PvxIkTNX78eG3ZskWDBw/WyJEjdfToUUnSkiVLtHr1aiUnJ2v9+vVq0qSJ/vjHP8oY487pAgAA4CpzW7g9c+aMwsPDNW7cONWtW1c33XST+vXrp++++04bNmzQuXPnNHz4cNWpU0dhYWF68MEHlZKSIklatmyZYmNjFRsbKx8fH/Xp00c2m02rVq2SJKWkpGjw4MFq2rSp/Pz8NGbMGGVmZmr79u3umi4AAABqQC13Xdjf318vvviiS9uRI0d04403ym63Kzg4WF5eXs6+0NBQLVu2TJJkt9sVGxvrcmxoaKjS09NVVFSkffv2KTQ01Nnn5+enxo0bKz09XdHR0ZesqaysTGVlZdUwOwD4Gd8pAFA9Kvp96rZw+5/S09P1/vvva/78+Vq7dq38/f1d+gMDA+VwOFReXi6Hw6GAgACX/oCAAO3bt0+5ubkyxly0Pycn51dryMjIqJ7JAMD/l5aW5u4SAOC/yjURbrdu3arhw4dr3LhxateundauXXvRcR4eHs6fL7d/tir7a202m+rUqVPp467YUnvNXxNAjfi1vxYBACquoKCgQguRbg+3X375pZ566ilNnTpVffv2lSQFBQVp//79LuMcDocCAwPl6empevXqyeFwXNAfFBTkHHOx/vr16/9qLV5eXi5bIQDgSvGdAgDVo6Lfp259WsK2bds0ceJEvf76685gK0nh4eHau3evSktLnW3p6emKiopy9u/cudPlXOf7fXx81KxZM9nt/14NPXPmjA4ePKjIyMirOyEAAAC4ldvCbWlpqaZMmaLx48erffv2Ln2xsbHy8/PT/PnzVVhYqO3bt2v58uVKTEyUJMXHx2vTpk3asGGDiouLtXz5cu3fv199+vSRJCUmJmrRokXKzMxUXl6eZs2apZCQEEVERNT4PAEAAFBz3LYtIS0tTZmZmUpKSlJSUpJL36effqoFCxbo2WefVXJysho0aKAxY8aoU6dOkn7eGztr1iy9+OKLys7O1p133qmFCxfqhhtukCQlJCToxIkTGjRokPLz8xUTE6N58+bV9BQBAABQwzwMbzZQQUGBdu/erZCQELfcUNbyqUU1fk0ANWPrzIfdXQIAWEJF85pb99wCAAAA1YlwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMtwe7j9+uuv1a5dO40ZM+aCvk8++US9e/dW8+bN1b9/f23cuNHZV15erjlz5qhLly5q3bq1hgwZokOHDjn7HQ6HRo8erXbt2ql9+/aaPHmyioqKamROAAAAcA+3htu33npLSUlJaty48QV9u3fv1sSJEzV+/Hht2bJFgwcP1siRI3X06FFJ0pIlS7R69WolJydr/fr1atKkif74xz/KGCNJmjp1qgoLC/Xxxx/rww8/VGZmpmbNmlWj8wMAAEDNcmu49fHx0fLlyy8abpctW6bY2FjFxsbKx8dHffr0kc1m06pVqyRJKSkpGjx4sJo2bSo/Pz+NGTNGmZmZ2r59u06ePKl169ZpzJgxCgoKUsOGDTVixAh9+OGHOnfuXE1PEwAAADWkljsv/vDDD1+yz263KzY21qUtNDRU6enpKioq0r59+xQaGurs8/PzU+PGjZWenq6zZ8/Ky8tLwcHBzv6wsDAVFBToxx9/dGn/pbKyMpWVlV3hrADg3/hOAYDqUdHvU7eG21/jcDgUEBDg0hYQEKB9+/YpNzdXxpiL9ufk5CgwMFB+fn7y8PBw6ZOknJycS14zIyOjGmcAAFJaWpq7SwCA/yrXbLiV5Nw/W5X+yx17MTabTXXq1Kn0cVdsqb3mrwmgRkRHR7u7BACwhIKCggotRF6z4bZevXpyOBwubQ6HQ0FBQQoMDJSnp+dF++vXr6+goCDl5eWprKxMXl5ezj5Jql+//iWv6eXl5RwPANWB7xQAqB4V/T51+6PALiU8PFw7d+50aUtPT1dUVJR8fHzUrFkz2e3/XvE8c+aMDh48qMjISIWEhMgYoz179rgc6+/vrzvuuKPG5gAAAICadc2G2/j4eG3atEkbNmxQcXGxli9frv3796tPnz6SpMTERC1atEiZmZnKy8vTrFmzFBISooiICAUFBal79+567bXXdPr0aR09elR//vOfNWDAANWqdc0uVgMAAOAKuTXpRURESJJKS0slSevWrZP08yqrzWbTrFmz9OKLLyo7O1t33nmnFi5cqBtuuEGSlJCQoBMnTmjQoEHKz89XTEyM5s2b5zz3n/70Jz377LPq0qWLrrvuOvXq1euiL4oAAACAdXiYqtx5ZTEFBQXavXu3QkJC3HJDWcunFtX4NQHUjK0zL/3IQwBAxVU0r12z2xIAAACAyiLcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAy6jl7gIAANZz8E8R7i4BwFVy+7R0d5fwq1i5BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlmHZcJudna0nnnhCMTExiouL08yZM1VeXu7usgAAAHAV1XJ3AVfLk08+qbCwMK1bt06nTp3S0KFD1aBBA/3v//6vu0sDAADAVWLJldv09HTt2bNH48eP1/XXX68mTZpo8ODBSklJcXdpAAAAuIosuXJrt9t1yy23KCAgwNkWFhamrKws5eXlyc/Pz2X8+e0K+fn5Kisrq9FaJen2er41fk0ANePs2bPuLsEtzvk3dncJAK4Sd32vFRUVSdJlt5laMtw6HA75+/u7tJ0Pujk5OReE2+LiYknSwYMHa6bA/zCle1O3XBfA1ZeRkeHuEtyjwwx3VwDgKnG4+XutuLj4giz3S5YMt5JkjKnw2ICAADVp0kQ+Pj7y9LTkTg0AAIDftPLychUXF7v8Zf5iLBlug4KC5HA4XNocDoc8PDwUFBR0wfhatWqpfv36NVQdAAAAquLXVmzPs+QyZXh4uI4cOaLTp08729LT03XnnXeqbt26bqwMAAAAV5Mlw21oaKgiIiI0e/Zs5eXlKTMzU++++64SExPdXRoAAACuIg9Tmc2pvyFHjx7V1KlT9a9//Ut+fn5KSEjQyJEj5eHh4e7SAAAAcJVYNtwC15Ls7GzNmDFD27dvV506ddSjRw+NGzeOGxgB/KZ9/fXXmjhxomJiYjRnzhx3lwNIsugNZcC1hjfmAbCat956S8uXL1fjxjzTGNcWlo2Aq4w35gGwIh8fH8Itrkms3AJXWWXfmAcAvwUPP/ywu0sALoqVW+Aqu9wb8wAAQPUh3AI1gPs2AQCoGYRb4Cqr7BvzAABA1RFugauMN+YBAFBzCLfAVcYb8wAAqDm8xAGoAbwxD4DVRERESJJKS0slSbVq/fwApvT0dLfVBEiEWwAAAFgI2xIAAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAKiE4OBgffXVV+4uo8Li4+M1d+5cSdKjjz6q1157zb0FAcBVVsvdBQAALu3zzz9XcHCwGjdufMXneuedd6qhIgC4trFyCwDXsDfeeEMHDhxwdxkA8JtBuAWASjpx4oQeeeQRRUZGqkePHsrIyHD2bd68WQ899JCaN2+uDh066M9//rOzzxijWbNmKTY2Vs2bN1e/fv307bffOvsHDRqkmTNnqnfv3nriiSfUp08f/fDDDxoxYoSefvrpCtX25z//We3bt1dMTIzLtc+ff9asWZKkrKwsDR48WK1atVLr1q01cuRI5eTkSJLKy8v1xhtvqGvXroqKitLvf/97bd261XmegwcPasiQIYqJiVFMTIzGjh2rM2fOOI996aWX1L59e0VHR6tPnz76+uuvK/T5AEB1INwCQCWlpKRo+vTp2rRpkxo0aKBXX31VknT06FGNGDFCiYmJ+u677/T2229r6dKlWr16tSRp5cqVWrFihVJSUvTdd9+pS5cuGjVqlMrKypznXrNmjZ5//nktXLhQq1atkiS9+eabevHFFy9b18aNG5WcnKzXX39dX331lYwxLsH7l5577jm1aNFCW7Zs0bp161RaWqr58+dLkt577z2tWbNGb7/9tr799lv17dtXw4cPV0FBgSRpypQpuvHGG/X1119r7dq1ysrK0ptvvumsf9OmTVq1apW2bt2qRx55RBMnTtS5c+cu+/kAQHUg3AJAJT3wwAO644475Ofnp86dOysrK0uS9PHHH6tZs2bq27evvLy8FBwcrISEBK1cuVKS1Lt3b61du1Y33XSTvLy81LNnT50+fVqHDx92njsyMlKRkZHy8PCodF3/+Mc/1LFjR7Vs2VI+Pj4aOnSovL29Lzr2zJkz8vX1Va1atRQQEKA333xTzzzzjCRp+fLlGjx4sJo0aSJvb28NGjRI/v7+2rBhgyQpOTlZ06dPl7e3t4KCgtShQwft3LnTed5atWqpdu3a8vLy0u9//3tt3LhR11133WU/HwCoDtxQBgCVdOuttzp/9vHx0blz5yT9/Of69PR0RUREOPuNMbrjjjskSYWFhXrhhRf01VdfKTc31zmmpKTE+fMtt9xS5bqOHTvmvJYkXXfddS61/tLIkSP11FNPacWKFWrfvr169eqlyMhI5zyef/55vfDCC87x5eXlOnLkiCRp586dmj17tvbu3atz586prKxM4eHhkqSePXtq5cqV6tixo+655x516tRJPXv2lKen52U/HwCoDoRbAKikS62q+vr6KjY2VgsWLLho/4wZM7R3714tWbJEjRs31qFDh9StWzeXMV5eXlWuq6SkRKWlpS5t5eXlFx3bqVMnbdiwQampqfriiy80cOBATZgwQQMHDpSvr6+SkpLUvXv3C47Lzc3VE088ocTERL311lvy8/PTa6+9pk2bNkmSAgMD9X//93/atm2b1q9frzfeeEN/+9vftGTJkst+PgBQHdiWAADV5Pbbb1dGRoaMMc62EydOOFdmd+zYoT59+qhJkyby8PCQ3W6v1uvfeOONOnr0qPP3kpISHTp06KJjc3JyVLduXfXo0UOzZ8/WjBkzlJKSIkm67bbbtHfvXpfxP/30kyTpxx9/VH5+voYMGSI/Pz9J0q5du5zjiouLVVhYqBYtWmjcuHH6+OOPlZGRoT179lz28wGA6kC4BYBq0rNnTzkcDr355psqKirSoUOH9Oijj+q9996T9PN2hvT0dJWUlCgtLU1r1qyRJB0/fvyS5/Tx8dGBAweUl5d32et37NhRGzdu1I4dO1RUVKR58+ZddOW2qKhI3bt318qVK1VaWqqioiLZ7XbdfvvtkqSEhAQtWbJEaWlpKisr0yeffKJevXrp8OHDuvnmm+Xp6anvv/9eBQUF+utf/6qTJ0/q5MmTKi0t1fPPP6+JEyfq9OnTMsbIbrervLxcN99882U/HwCoDoRbAKgm9erV05tvvqkvvvhCrVu31sCBAxUXF6dHH31UkjRu3DhlZmaqTZs2mjNnjqZOnapu3bppxIgRl1zFTUhI0CuvvKKnnnrqste///779fDDD2vYsGGKjY2Vt7e3oqOjLxjn6+ur119/XX/961/VqlUrderUSUePHtW0adMkSQMGDNAf/vAHjRw5Ui1bttTbb7+tefPm6eabb1bDhg01duxYPfPMM4qLi1Nubq5mzZqlkpIS/eEPf9C4cePk6emp7t27q0WLFnr++ec1e/ZsBQUFXfbzAYDq4GF++fchAAAA4DeMlVsAAABYBk9LAIDfgLVr12rChAmX7G/durXeeeedGqwIAK5NbEsAAACAZbAtAQAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGf8PEoRpyM8aXMIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "categorical_columns = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status','hypertension', 'heart_disease']\n",
        "for column in categorical_columns:\n",
        "    plt.figure(figsize=(8, 4))\n",
        "    sns.countplot(data=dataset, x=column)\n",
        "    plt.title(f'{column} Count')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJj4H5MTY3on"
      },
      "source": [
        "> ### ***Heatmap***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "238_m585Y2i2"
      },
      "source": [
        "A heatmap is a graphical representation of data where individual values contained in a matrix are represented as colors. It is often used to visualize complex data sets with two variables: the value of each variable for each observation is represented as color on a map. This can be useful to identify patterns and correlations in the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "Gyw5OkqN1eUG",
        "outputId": "c89925e6-6753-421b-c1b4-a9a5eb7ff9b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Correlation\n",
        "numeric_columns = [\"bmi\", \"avg_glucose_level\", \"heart_disease\", \"hypertension\", \"age\", \"stroke\"]\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(dataset[numeric_columns].corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
        "plt.title('Correlation matrix between numerical variables')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfFgK1BPZzRU"
      },
      "source": [
        "We can observe that we have an overall weak-to-moderate positive correlation between the variables. \\\\\n",
        "With a more in-depth analysis we can see that we have a moderate correlation between age and bmi, age and stroke, age and hypertension, age and haeart_disease, age and glucose_level. Basically with the 'age' variable and all the other ones we took into consideration for the heatmap.\n",
        "\n",
        "These are all results that we expected, since it's natural that older people usually have more health problems and are therefore more at risk of stroke and similar.\n",
        "\n",
        "We can also observe that bmi and stroke have a really weak correlation, this means that having an high or low BMI doesn't necessarly influence the possibility of having a stroke.\n",
        "\n",
        "It's important to note, though, that correlation does not necessarily imply causation. The results of the heatmap provide evidence for a potential association, but further investigation is needed to determine the exact relationship between these variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yv-qbu31T6w6"
      },
      "source": [
        "> ### ***Pieplot***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwmu_yZtPH36"
      },
      "source": [
        "Now we can move forward with our statistical analisys and perform a pieplot on the 'stroke' variable. \\\\\n",
        "We decided to use a piechart for this variable because it's a good way to visualize and have an idea of how many people had or not a stroke."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ve7tnQ19PqvV"
      },
      "outputs": [],
      "source": [
        "df=dataset.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "Ocbl7wy-2gyK",
        "outputId": "cf41f0ee-4076-48d7-aa3c-57bdfd3e1d95"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "df['stroke_label'] = df['stroke'].map({1: 'Stroke', 0: 'No Stroke'})\n",
        "stroke_distribution = df['stroke_label'].value_counts()\n",
        "plt.pie(stroke_distribution, labels=stroke_distribution.index, autopct='%1.1f%%', colors=['#1f77b4', '#ff7f0e'])\n",
        "plt.title('Stroke distribution')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7we1QAy8CfGo"
      },
      "source": [
        "Here we can clearly see how many people do not have stroke in our dataset, 95.1%, which is quite a lot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM3gauttVdFa"
      },
      "source": [
        "Given the previous graphs it would be interesting to see the variables ratio with respect to the stroke:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6Gy6Bum84Jjd",
        "outputId": "d948e42d-5896-4cb9-8bf7-edfe0211852c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for column in categorical_columns:\n",
        "    plt.figure(figsize=(10, 6))\n",
        "\n",
        "    # Compute the ratio between the number of y with that mode and the number of those with stroke=0\n",
        "    counts_stroke_0 = df[df['stroke'] == 0][column].value_counts()\n",
        "    total_stroke_0 = df[df['stroke'] == 0].shape[0]\n",
        "    ratio_stroke_0 = counts_stroke_0 / total_stroke_0\n",
        "\n",
        "    # Compute the ratio between the number of y with that mode and the number of those with stroke=1\n",
        "    counts_stroke_1 = df[df['stroke'] == 1][column].value_counts()\n",
        "    total_stroke_1 = df[df['stroke'] == 1].shape[0]\n",
        "    ratio_stroke_1 = counts_stroke_1 / total_stroke_1\n",
        "\n",
        "    # Unify the previously computed dataframes\n",
        "    ratio_df = pd.DataFrame({\n",
        "        'Stroke 0': ratio_stroke_0,\n",
        "        'Stroke 1': ratio_stroke_1\n",
        "    })\n",
        "\n",
        "    ratio_df.plot(kind='bar', color=['#1f77b4', '#ff7f0e'])\n",
        "    plt.title(f'{column} ratio with respect to stroke')\n",
        "    plt.ylabel('Ratio')\n",
        "    plt.xlabel(column)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpPgSaqAmcjI"
      },
      "source": [
        "To interpret this data, let's start by examining the gender ratio concerning stroke. We find a higher count of female subjects, and when comparing stroke subtypes, the male to female gender ratio is significantly higher in Stroke 1 (those who have had a stroke) compared to Stroke 0 (those who haven't had a stroke). This suggests that certain factors related to the severity or type of stroke might be influencing the gender ratio. Furthermore, the data indicates a higher prevalence of male patients in stroke cases.\n",
        "\n",
        "Next, let's focus on the impact of smoking habits. Research indicates that smokers are significantly more prone to experiencing a stroke compared to those who abstain from smoking. \\\\\n",
        "According to a study from the British Heart Foundation, smokers are nearly twice as likely to face a stroke as non-smokers. This heightened risk is attributed to the harmful effects of smoking on blood vessels and the subsequent decrease in blood flow to the brain caused by smoking [[3]](https://www.stroke.org.uk/resources/smoking-and-risk-stroke).\\\\\n",
        "Moreover, another study discloses that the likelihood of suffering a stroke rises by 12% for each additional five cigarettes consumed daily [[4]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6708836/). \\\\\n",
        "These studies provide evidence supporting the observation reflected in the barplot, suggesting a strong relationship between smoking and the risk of stroke.\n",
        "\n",
        "Similarly, patients with high blood pressure are significantly more likely to experience a stroke compared to those without hypertension. \\\\\n",
        "A study published in the Journal of Clinical Hypertension states that high blood pressure is the single most important culprit behind cerebrovascular diseases and the most prevalent modifiable risk factor [[5]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8030455/).\n",
        "\n",
        "Looking at heart disease versus stroke, patients with heart disease have a higher probability of experiencing a stroke compared to those without heart disease. \\\\\n",
        "A study from the National Center for Biotechnology Information (NCBI) supports this, stating that heart disease is a significant risk factor for stroke [[6]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9721101/). The article further explains that stroke patients with heart failure exhibit higher neurological deficits at admission and discharge from the hospital and this phenomenon continues even three months following the stroke.\n",
        "\n",
        "Based on the graphs, we can conclude that married patients have a higher probability of experiencing a stroke than single patients, and those living in urban areas have a higher probability of experiencing a stroke compared to those living in rural areas. This might be due to other factors associated with the risk of stroke rather than directly related to being married or living in urban areas.\n",
        "\n",
        "Finally, for the work_type variable, from the bar plot we can see that self-employed or professionals have a slightly higher risk of experiencing a stroke compared to those in technical, sales, or administrative jobs. However, these results might not be directly related to the work type, but rather to other associated factors that have a higher correlation with stroke."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PfCP6_P9KTP"
      },
      "source": [
        "--------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SmIv4wDWrj0"
      },
      "source": [
        "# Train and split\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ISvrm1ful5o"
      },
      "source": [
        "Before diving into the splitting of the data we will encode the 'Gender', 'Ever Married', and 'Residence Type' variables using 0 and 1. This step is crucial for applying machine learning algorithms to non-numerical variables. \\\\\n",
        "The reason behind this is that many machine learning algorithms require numerical input. Non-numerical or categorical data, such as gender, marital status, and residence type, can't be processed directly by these algorithms. To overcome this, we convert these categorical variables into a format that can be understood by the machine learning algorithms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lWe7vySNuhOE",
        "outputId": "fb17b860-6116-4469-948d-75477f8ba9cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  gender   age  hypertension  heart_disease  ever_married  \\\n",
              "0      9046       0  67.0             0              1             1   \n",
              "1     51676       1  61.0             0              0             1   \n",
              "2     31112       0  80.0             0              1             1   \n",
              "3     60182       1  49.0             0              0             1   \n",
              "4      1665       1  79.0             1              0             1   \n",
              "...     ...     ...   ...           ...            ...           ...   \n",
              "5105  18234       1  80.0             1              0             1   \n",
              "5106  44873       1  81.0             0              0             1   \n",
              "5107  19723       1  35.0             0              0             1   \n",
              "5108  37544       0  51.0             0              0             1   \n",
              "5109  44679       1  44.0             0              0             1   \n",
              "\n",
              "          work_type  Residence_type  avg_glucose_level        bmi  \\\n",
              "0           Private               1             228.69  36.600000   \n",
              "1     Self-employed               0             202.21  28.893237   \n",
              "2           Private               0             105.92  32.500000   \n",
              "3           Private               1             171.23  34.400000   \n",
              "4     Self-employed               0             174.12  24.000000   \n",
              "...             ...             ...                ...        ...   \n",
              "5105        Private               1              83.75  28.893237   \n",
              "5106  Self-employed               1             125.20  40.000000   \n",
              "5107  Self-employed               0              82.99  30.600000   \n",
              "5108        Private               0             166.29  25.600000   \n",
              "5109       Govt_job               1              85.28  26.200000   \n",
              "\n",
              "       smoking_status  stroke  \n",
              "0     formerly smoked       1  \n",
              "1        never smoked       1  \n",
              "2        never smoked       1  \n",
              "3              smokes       1  \n",
              "4        never smoked       1  \n",
              "...               ...     ...  \n",
              "5105     never smoked       0  \n",
              "5106     never smoked       0  \n",
              "5107     never smoked       0  \n",
              "5108  formerly smoked       0  \n",
              "5109          Unknown       0  \n",
              "\n",
              "[5109 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ffe771a-d894-4cb6-b28b-ac6df43e16c9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>0</td>\n",
              "      <td>202.21</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>0</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.500000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>1</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.400000</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>1</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>0</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>18234</td>\n",
              "      <td>1</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>83.75</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>44873</td>\n",
              "      <td>1</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>1</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>19723</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>0</td>\n",
              "      <td>82.99</td>\n",
              "      <td>30.600000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>37544</td>\n",
              "      <td>0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>0</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>44679</td>\n",
              "      <td>1</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>1</td>\n",
              "      <td>85.28</td>\n",
              "      <td>26.200000</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5109 rows × 12 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# RUN ONLY ONE TIME\n",
        "run = True\n",
        "if(run):\n",
        "\n",
        "  # GENDER: F/M --> 1/0\n",
        "  dataset[\"gender\"] = dataset[\"gender\"].apply(lambda x: 1 if x==\"Female\" else 0)\n",
        "\n",
        "  # EVER_MARRIED: YES/NO --> 1/0\n",
        "  dataset[\"ever_married\"] = dataset[\"ever_married\"].apply(lambda x: 1 if x==\"Yes\" else 0)\n",
        "\n",
        "  # RESIDENCE_TYPE: URBAN/RURAL --> 1/0\n",
        "  dataset[\"Residence_type\"] = dataset[\"Residence_type\"].apply(lambda x: 1 if x==\"Urban\" else 0)\n",
        "\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqtn05uvIQia"
      },
      "source": [
        "We will then proceed with the split of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "APGyvaLcX0fe"
      },
      "outputs": [],
      "source": [
        "X = dataset.drop(['id', 'stroke'], axis = 1)\n",
        "y = dataset['stroke']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UAkjNmTNt-Q"
      },
      "source": [
        "Then we proceed to define a function that normalises the inputed dataframe. \\\\\n",
        "Normalization is a technique that scales the features to a fixed range, usually between 0 and 1. This is useful when the features have different scales, as it ensures that all features contribute equally to the model, regardless of their original scale."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ejePasPyDXnG"
      },
      "outputs": [],
      "source": [
        "def norm_X(X_total):\n",
        "    # transform the dataframe in dictionary to perform feature extraction\n",
        "    X_total = X_total.to_dict('records')\n",
        "\n",
        "    v = DictVectorizer(sparse = False, dtype = float)\n",
        "    X_total = v.fit_transform(X_total)\n",
        "    return X_total, v"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HRKhq76WBbaA"
      },
      "outputs": [],
      "source": [
        "#normalize the features\n",
        "X_big_array = X.copy()\n",
        "X_big_array, v = norm_X(X_big_array)\n",
        "\n",
        "X_norm = pd.DataFrame(X_big_array, columns = v.get_feature_names_out())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqSv53btOvm3"
      },
      "source": [
        "Then we can divide in train and test to then apply the machine learning algorithms for the prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROURo0p9BIS8"
      },
      "source": [
        "After applying the KNN algorithm, Logistic Regression and Support vector machine to our data we noticed that they all gave back very bad confusion matrices. This is probably due to the fact that in out dataset we have unbalanced classes: 95.1% of our subjects didn't have a stoke. \\\\\n",
        "To try and fix this we applied SMOTE (Synthetic Minority Over-sampling Technique) on the data and re-run the algorithms.\n",
        "\n",
        "\\\\\n",
        "The SMOTE technique is used to handle imbalanced datasets in machine learning, since this imbalance can lead to a model that is biased towards the majority class and performs poorly on the minority class. \\\\\n",
        "Instead of simply duplicating existing instances from the minority class, SMOTE synthesizes new instances that are similar to the existing instances but not identical. This is done by selecting two or more similar instances from the minority class, drawing a line between them, and generating a new instance along this line. \\\\\n",
        "When using this SMOTE technique, thought, it should be taken into account that applying it to the test data could lead to data leakage, where information from the test set influences the training process, resulting in overly optimistic performance estimates [[7]](https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/) [[8]](https://www.nature.com/articles/s41598-021-03430-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "2Y0jCUA60AL0"
      },
      "outputs": [],
      "source": [
        "# Split your data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_big_array, y.array, test_size=0.33, random_state=100)\n",
        "\n",
        "# Initialize SMOTE\n",
        "oversample = SMOTE()\n",
        "\n",
        "# Apply SMOTE only to the training data\n",
        "X_train_res, y_train_res = oversample.fit_resample(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsP43EFUA--I",
        "outputId": "9a11c3b9-7768-43f4-9f12-4be186ab5621"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The non-SMOTE train data has 3423 observations\n",
            "The SMOTE train data has 6506 observations\n",
            "So we can see that the train data, after applying the SMOTE, get doubled in number\n"
          ]
        }
      ],
      "source": [
        "print(\"The non-SMOTE train data has \" + str(len(X_train) ) + \" observations\") #len of the non-SMOTE train data : 3423\n",
        "print(\"The SMOTE train data has \" + str(len(X_train_res) ) + \" observations\") #len of the SMOTE train data : 6506\n",
        "print(\"So we can see that the train data, after applying the SMOTE, get doubled in number\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4oKKbYjD5pS"
      },
      "source": [
        "We decided to use the KNN algorithm, Logistic Regression and Support vector machine because they have the following strong points:\n",
        "1. **K-Nearest Neighbors (KNN):** \\\\\n",
        "The algorithm is simple and easy to understand.\n",
        "It can be used for classification or regression tasks.\n",
        "The algorithm can be effective in high-dimensional spaces.\n",
        "However, the performance of KNN can be negatively impacted by changes in the data distribution and may not be suitable for large datasets due to its complexity in computation.\n",
        "\n",
        "2. **Logistic Regression:** \\\\\n",
        "The strength of logistic regression lies in its ability to predict a binary outcome (0 or 1) from one or more input variables. Logistic regression can also handle data that is not linearly separable, thanks to its ability to work with the log-odds of the outcome. The model is robust and can provide a good starting point for prediction. However, logistic regression requires a careful choice of features and scaling of data, as it assumes linear relationships between input features and log-odds of the outcome.\n",
        "\n",
        "3. **Support Vector Machines (SVM):** \\\\\n",
        "The strength of SVM lies in its ability to find optimal hyperplanes that maximize the margin between different classes. SVM is particularly effective at dealing with high-dimensional data. The decision function provided by SVMs is also particularly useful for multiclass classification. However, SVMs can be sensitive to the selection of hyperparameters (such as the choice of kernel and regularization parameter C). They can also be slow to train for large datasets, especially when using complex kernels.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0uOAczFGj5U"
      },
      "source": [
        "Now let's apply them to the data:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdE_xDUzL6dc"
      },
      "source": [
        "### *k-neighbors method*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tynDrtIKeLG0"
      },
      "source": [
        "KNN is a type of algorithm used in machine learning that helps in classifying new data points based on the similarity of the data points in the training set. It does this by looking at the 'k' closest data points in the training set to the new data point and making a prediction based on these 'k' closest points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbvFE_eRFHln"
      },
      "source": [
        "Before starting with the knn method, we will define a function that computes the performance evaluation metrics that we will need later to evaluate the various model's performance we will use"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "0lfbTCyiRlGD"
      },
      "outputs": [],
      "source": [
        "def calculate_performance(mat):\n",
        "  # values of our confusion matrix\n",
        "  TN, FP, FN, TP = mat.ravel()\n",
        "\n",
        "  # Calculate Precision, Recall e F1-Score\n",
        "  precision = TP / (TP + FP)\n",
        "  recall = TP / (TP + FN)\n",
        "  f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "\n",
        "  print(f'Precision: {precision:.4f}')\n",
        "  print(f'Recall: {recall:.4f}')\n",
        "  print(f'F1-Score: {f1_score:.4f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJrseJl_jWg1"
      },
      "source": [
        "*Elbow method for optimal number of neighbors*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlSBNQ15EUYm"
      },
      "source": [
        "Before applying the KNN regressor we can use the Elbow Method to find the optimal number of neighbors. \\\\\n",
        "The elbow method is a technique used to determine the optimal number of components in Principal Component Analysis (PCA). It is a method that involves plotting the explained variance as a function of the number of components and identifying the point where the curve begins to bend, often referred to as the \"elbow\" of the curve. This point is typically chosen as the optimal number of components to retain, as adding more components does not significantly increase the explained variance. \\\\\n",
        "This method can be adapted to the case of finding the optimal number of neighbors following the exact same reasoning, but with the the test error instead of the explained variance. So basically, our optimal numer of neighbors is the one that minimizes the test error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "XWJdkPfEgLXv",
        "outputId": "b552785d-4998-4fb0-9983-ca643b93d638"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# List to hold test errors\n",
        "errors = []\n",
        "\n",
        "# Range of possible values for n_neighbors\n",
        "possible_values = range(1, 50)\n",
        "\n",
        "# For each possible value of n_neighbors\n",
        "for n in possible_values:\n",
        "   # Create a KNN regressor with n_neighbors = n\n",
        "   knn = KNeighborsRegressor(n_neighbors=n)\n",
        "\n",
        "   # Fit the model to the training data\n",
        "   knn.fit(X_train, y_train)\n",
        "   #knn.fit(X_train_res, y_train_res) #for SMOTE train set\n",
        "\n",
        "   # Use the fitted model to predict the test data\n",
        "   y_pred = knn.predict(X_test)\n",
        "\n",
        "   # Compute the test error\n",
        "   mse = mean_squared_error(y_test, y_pred)\n",
        "   errors.append(mse)\n",
        "\n",
        "# Plot the test errors\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(possible_values, errors, color='blue')\n",
        "plt.scatter(possible_values, errors, color='red')\n",
        "plt.xlabel('Number of Neighbors')\n",
        "plt.ylabel('Test Error')\n",
        "plt.axvline(x=4, color='black', linestyle='--')\n",
        "plt.axhline(y= 0.0495, color='black', linestyle='--')\n",
        "#plt.axvline(x=4 , color='black', linestyle='--') #for SMOTE train set\n",
        "#plt.axhline(y=0.157 , color='black', linestyle='--') #for SMOTE train set\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrVEi8P3h6G1"
      },
      "source": [
        "As we can see from the graph, the number of neighbors that minimzes the test error is equal to 4, for both the SMOTE (with minimal test error = 0.157) and non-SMOTE (with minimal test error =0.0495) train data. \\\\\n",
        "Now we can proceed with the KNN regressor:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Sv0m7DGYXnhV"
      },
      "outputs": [],
      "source": [
        "knn_regressor = KNeighborsRegressor(n_neighbors=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7-6gwk4-xSpI",
        "outputId": "51d9e46c-3c1c-4a15-d1c1-3283cffb9e3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=4)"
            ],
            "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>KNeighborsRegressor(n_neighbors=4)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Train the classifier on the training data\n",
        "#knn_regressor.fit(X_train, y_train)\n",
        "knn_regressor.fit(X_train_res, y_train_res) #SMOTE train set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "79ifm7W3x_ap"
      },
      "outputs": [],
      "source": [
        "# Predictions on the dataset's data\n",
        "y_pred = knn_regressor.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "XTES4mMT87kK"
      },
      "outputs": [],
      "source": [
        "# Convert y_pred to int type\n",
        "y_pred = y_pred.astype(int)\n",
        "\n",
        "#performance metrics to evaluate the model's performance:\n",
        "accuracy_knn = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred) #generates a text report showing the main classification metrics\n",
        "mat_knn = confusion_matrix(y_test, y_pred) #computes the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "IOwbKGOCyAFk",
        "outputId": "2e8dab1b-cb59-444c-f324-4011c6500e3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Confusion matrix\n",
        "\n",
        "sns.set(font_scale=1.5)\n",
        "sns.heatmap(mat_knn, square=True, annot=True, cbar=False, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"KNN confusion matrix\")\n",
        "plt.xlabel('Predicted value')\n",
        "plt.ylabel('True value')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbrreXoBDng1"
      },
      "source": [
        "Now let's compute some performance evaluation metrics:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyBYhZn4UwoD",
        "outputId": "71822f62-e316-4602-88a2-30c69bb355ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.1615\n",
            "Recall: 0.3291\n",
            "F1-Score: 0.2167\n"
          ]
        }
      ],
      "source": [
        "#The following computes the precision, recall and the F1_score\n",
        "calculate_performance(mat_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VV6XAgvLpoPW",
        "outputId": "2a489a98-bdd0-4b18-e0ee-79fca712b28f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8884934756820878\n"
          ]
        }
      ],
      "source": [
        "#Accuracy metric\n",
        "print(accuracy_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDYnKSK3M2Nt"
      },
      "source": [
        "### *Support vector machine*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5Hy5m_Lea_2"
      },
      "source": [
        "Support Vector Machine (SVM) is a powerful machine learning model used for both classification and regression problems. It works by finding a hyperplane that best separates the data points of different classes. The goal is to find the optimal hyperplane that maximizes the margin between the closest points of different classes. Essentially, it's a decision boundary that helps separate the classes in the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "ePLXLptU49SF"
      },
      "outputs": [],
      "source": [
        "# create the SVM classifier\n",
        "svm_classifier = SVC(kernel='linear', C=1.0)  # Puoi personalizzare il kernel e i parametri C a seconda delle tue esigenze\n",
        "\n",
        "# Train the classifier on the training data\n",
        "#svm_classifier.fit(X_train, y_train)\n",
        "svm_classifier.fit(X_train_res, y_train_res) #SMOTE train set\n",
        "\n",
        "# Predictions on the test data\n",
        "y_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "\n",
        "# Evaluate the model's performance with the following performance evaluation metrics\n",
        "accuracy_svm = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred) #generates a text report showing the main classification metrics\n",
        "mat_svm = confusion_matrix(y_test,y_pred) #computes the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "pg4irlFZS1vB",
        "outputId": "f6d15b48-d717-4f35-85cd-c8ae6fbebb27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Confusion matrix\n",
        "sns.set(font_scale=1.5)\n",
        "sns.heatmap(mat_svm, square=True, annot=True, cbar=False, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"SVM confusion matrix\")\n",
        "plt.xlabel('Predicted value')\n",
        "plt.ylabel('True value')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loyuDJZWUj4-",
        "outputId": "8fb332af-62cb-4276-b38c-d3a7232d0950"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.1269\n",
            "Recall: 0.8481\n",
            "F1-Score: 0.2208\n"
          ]
        }
      ],
      "source": [
        "#The following computes the precision, recall and the F1_score\n",
        "calculate_performance(mat_svm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GXt6D18qzgA",
        "outputId": "f62fe710-2958-4d20-f91a-17cf6fe33b67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7194543297746144\n"
          ]
        }
      ],
      "source": [
        "#Accuracy metric\n",
        "print(accuracy_svm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKP8OHYEV-mH"
      },
      "source": [
        "### *Logistic Regression*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAWydqIzeSnY"
      },
      "source": [
        "Logistic regression is a statistical model that is used to predict the probability of an event occurring. It's commonly used in binary classification problems where the output is either success or failure. It uses a logistic function to model the probability of the occurrence of an event, given a set of independent variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZSaWFPTZrn3",
        "outputId": "109ac608-0e9a-467d-e52c-170b74e082ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ],
      "source": [
        "# create the Logistic Regression classifier\n",
        "clf = LogisticRegression(random_state=0)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "#clf.fit(X_train, y_train)\n",
        "clf.fit(X_train_res, y_train_res) #SMOTE train set\n",
        "\n",
        "# Predicions on the test data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Evaluate the model's performance with the following performance evaluation metrics\n",
        "accuracy_clf = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred) #generates a text report showing the main classification metrics\n",
        "mat_lr = confusion_matrix(y_test,y_pred) #computes the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "Lgb4ysTJZ7_z",
        "outputId": "6a8d49c2-6e95-422e-ed98-c814e64d276a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Confusion matrix\n",
        "sns.set(font_scale=1.5)\n",
        "sns.heatmap(mat_lr, square=True, annot=True, cbar=False, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Logistic Regression confusion matrix\")\n",
        "plt.xlabel('Predicted value')\n",
        "plt.ylabel('True value')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-poW0F4CUS89",
        "outputId": "c0036394-52a8-41a0-f6d6-de3ee63f68af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.1335\n",
            "Recall: 0.8481\n",
            "F1-Score: 0.2306\n"
          ]
        }
      ],
      "source": [
        "#The following computes the precision, recall and the F1_score\n",
        "calculate_performance(mat_lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VdgMGbpqnWU",
        "outputId": "bd169495-91a4-40cf-f30c-44261ffc2079"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7348754448398577\n"
          ]
        }
      ],
      "source": [
        "#Accuracy metric\n",
        "print(accuracy_clf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6JKhTljo7BE"
      },
      "source": [
        "From the results of both the confusion matrices and the performance evaluation metrics we can undoubtly see that the algorithms aplied on the SMOTE dataset have way worse accuracy than without applying SMOTE. This kind of result can be due to:\n",
        "\n",
        "**Overfitting:** One possibility is that we might be oversampling too much. When generating synthetic samples, noise or bias can be introduced into your dataset, leading to overfitting. Overfitting occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. \\\\\n",
        "**Overlapping:** Another possibility is that SMOTE is generating synthetic points that overlap with the real data, causing issues with the model's ability to distinguish between different classes. This could potentially harm the model's performance. \\\\\n",
        "**Model sensitivity:** Some models might be sensitive to the presence of synthetic samples. If this is the case, even though the model is being oversampled, the synthetic samples might still cause the model to underperform."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_eXyv2xFTDQ"
      },
      "source": [
        "--------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLvdVhgHEoW_"
      },
      "source": [
        "After trying out machine learning algorithms on the smoothed dataset and computing performance evaluation metrics, since the smoothing creates a false datset an alternative solution to fix the unbalanced dataset could be model combination.\n",
        "\n",
        "\\\\\n",
        "Model combination, also known as stacking or model averaging, is an ensemble learning method where the predictions of multiple base models are combined to improve the predictive performance of the model. This technique is often used when dealing with multiple models that are based on different assumptions or algorithms, and it aims to improve the prediction performance by leveraging the strengths of all models.\n",
        "\n",
        "In the context of predicting whether someone has a stroke or not, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) are used. Each of these models has its own strengths and weaknesses, and combining them could potentially improve the overall prediction performance.\n",
        "\n",
        "The basic steps of model combination are as follows:\n",
        "\n",
        "1. **Train individual models**: First, train a Logistic Regression, KNN, and SVM model on your dataset. Each of these models would learn to predict whether a person has a stroke based on the input features.\n",
        "2. **Generate predictions**: Next, use each of these models to generate predictions on your test data.\n",
        "3. **Combine predictions**: Finally, combine these predictions in some way to generate the final prediction. One simple way to do this is to take the majority vote of the predictions from each model.\n",
        "\n",
        "This approach is a powerful technique that can often improve the prediction performance of the models. By combining the predictions of multiple models, it can often achieve better results than any single model, especially when the models are based on different assumptions or algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfeGQZ3BFf74"
      },
      "source": [
        "So we will combine the strength of the KNN algorithm, Logisitc Regression and to have a model with improved predictive model and that will, hopefully, help us solve the problem of the unbalanced dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "v0aEHVWhh1sY"
      },
      "outputs": [],
      "source": [
        "#X_train_res, y_train_res -> smoothed\n",
        "#X_big_array, y.array -> non smoothed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ya22nPgyoN2M",
        "outputId": "b6e44536-870c-412f-912a-f7412aa48dfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9531435349940688\n",
            "Precision: nan\n",
            "Recall: 0.0000\n",
            "F1-Score: nan\n",
            "None\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-1db635bc0ff9>:6: RuntimeWarning: invalid value encountered in long_scalars\n",
            "  precision = TP / (TP + FP)\n"
          ]
        }
      ],
      "source": [
        "# Create the base classifiers\n",
        "base_models = [\n",
        "    ('logistic', clf),\n",
        "    ('knn', knn_regressor),\n",
        "    ('svm', svm_classifier)\n",
        "]\n",
        "\n",
        "# Create the stacking classifier\n",
        "model = StackingClassifier(estimators=base_models, final_estimator=LogisticRegression(solver='lbfgs', max_iter=1000))\n",
        "model.fit(X_train, y_train)\n",
        "#model.fit(X_train_res, y_train_res) #SMOTE train set\n",
        "\n",
        "# Make predictions on the test data\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "mat_model_comb = confusion_matrix(y_test,predictions)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(calculate_performance(mat_model_comb))\n",
        "\n",
        "#training on the SMOTE training set gives an accuracy of 85.82%, a precision of 0.1327, recall of 0.3797, F1-Score of 0.1967\n",
        "#training on the non-SMOTE training set gives an accuracy of  0.9531435349940688, the other metrics are nan (precision and F1) and 0 (recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbPDsPF4I7ax"
      },
      "source": [
        "We can now compare the accuracy obtained in the \"single\" models and the one obatined from the model combination above. \\\\\n",
        "Accuracy can be used for comparison of everything, while the other metrics (since they come up as nan for the non-SMOTE training set) can be used for an 'internal' evaluation of only the algorithms applied to SMOTE.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KN_hlkyJLgyp"
      },
      "source": [
        "|  |  | non-SMOTE training set | | |\n",
        "|---------|:---------:|:---------:|:---------:|:---------:|\n",
        "|| Accuracy | Precision | Recall | F1-score |\n",
        "| KNN algorithm | 95.25%  | 0.0 | 0.0 | nan |\n",
        "| Support vector machine | 95.31% | nan | 0.0 | nan |\n",
        "| Logistic regression | 95.31% | nan | 0.0 | nan |\n",
        "| Model combination| 95.31% | nan | 0.0 | nan |\n",
        "\n",
        "\n",
        "| | | SMOTE training set | | |\n",
        "|---------|:---------:|:---------:|:---------:|:---------:|\n",
        "|| Accuracy | Precision | Recall | F1-score |\n",
        "| KNN algorithm | 88.79% | 15.19% | 30.38% | 20.25% |\n",
        "| Support vector machine | 72.36% | 12.86% | 84.81% | 22.33% |\n",
        "| Logistic regression | 74.20% | 13.52% | 83.54% | 23.28% |\n",
        "| Model combination| 85.82% |13.27% | 37.97% | 19.67% |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1EvFcVkPgHF"
      },
      "source": [
        "Based on the performance metrics above we can draw some conclusions:\n",
        "\n",
        "1. **KNN**: Without SMOTE, the model achieves an accuracy of 95.25%, but the precision, recall, and F1-score are zero, indicating that the model is not performing well at identifying the positive class. With SMOTE, the accuracy drops to 88.79%, but the precision, recall, and F1-score increase, suggesting that the model is better at identifying the positive class.\n",
        "\n",
        "2. **Logistic Regression**: Similar to KNN, the performance of Logistic Regression improves when SMOTE is applied. Without SMOTE, the accuracy is 95.31% but the precision, recall, and F1-score are NaN, suggesting that the model is struggling to identify the positive class. With SMOTE, the accuracy drops to 74.20%, but the precision, recall, and F1-score improve, indicating that the model is now better at identifying the positive class.\n",
        "\n",
        "3. **Support Vector Machine (SVM)**: The performance of SVM also improves when SMOTE is applied. Without SMOTE, the accuracy is 95.31% but the precision, recall, and F1-score are NaN, suggesting that the model is struggling to identify the positive class. With SMOTE, the accuracy drops to 72.36%, but the precision, recall, and F1-score improve, indicating that the model is now better at identifying the positive class.\n",
        "\n",
        "4. **Model Combination**: The \"Model combination\" achieved an accuracy of 95.31% without SMOTE and 85.82% with SMOTE. This suggests that the combination of the models performs better than the individual models, especially when SMOTE is applied.\n",
        "\n",
        "It appears that SMOTE is helping improve the precision, recall, and F1-score of the models, which are crucial metrics for evaluating the performance of classification models, especially when dealing with imbalanced datasets. However, the accuracy of the models is slightly lower when SMOTE is applied compared to when it isn't. This suggests that while SMOTE can improve the models' ability to identify the positive class, it may also introduce some amount of noise or bias into the dataset.\n",
        "\n",
        "To summarize, the Support Vector Machine (SVM) model seems to be the most effective for predicting stroke occurrences based on the provided metrics. It boasts a high accuracy of 72.36% and a superior recall score of 84.81%. High recall implies a low false negative rate, making the SVM model a strong choice for identifying individuals at risk of a stroke.\n",
        "\n",
        "However, while the SVM model excels in recall, its precision is slightly lower compared to other models. Precision refers to the percentage of actual positive cases correctly identified as such, so it's possible that the SVM model may occasionally predict a positive result when the person doesn't actually have a stroke. Although this might be concerning, it's important to note that having false positives isn't as critical as having false negatives, which occur when a stroke goes undetected.\n",
        "\n",
        "Therefore, considering high recall as a priority, the SVM model would be the preferred choice."
      ]
    }
  ],
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