Health_Care_Prediction_for_Diabetic_Patients.ipynb

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      "cell_type": "markdown",
      "source": [
        "# ****Health Care Prediction for Diabetic Patients - Case Study****"
      ],
      "metadata": {
        "id": "6D0HwIM1J3Jm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Context**"
      ],
      "metadata": {
        "id": "wCN-336IKjxK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage."
      ],
      "metadata": {
        "id": "JDkbfrzFKqAK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Problem Statement**"
      ],
      "metadata": {
        "id": "sxjLrrJKZmNe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?"
      ],
      "metadata": {
        "id": "i1X2GbfaZqEb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Dataset Description**"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "#### The dataset includes various medical predictor variables and one target variable, \"Outcome.\" The predictor variables encompass essential health metrics, such as the number of pregnancies, plasma glucose concentration, diastolic blood pressure, triceps skinfold thickness, insulin levels, body mass index (BMI), diabetes pedigree function, and age."
      ],
      "metadata": {
        "id": "pHuU6Yh5LC1Q"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "### **Predictor Values**"
      ],
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      }
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      "source": [
        "1. **Pregnancies:**\n",
        "   - Number of times pregnant\n",
        "\n",
        "2. **Glucose:**\n",
        "   - Plasma glucose concentration at 2 hours in an oral glucose tolerance test\n",
        "\n",
        "3. **BloodPressure:**\n",
        "   - Diastolic blood pressure (mm Hg)\n",
        "\n",
        "4. **SkinThickness:**\n",
        "   - Triceps skinfold thickness (mm)\n",
        "\n",
        "5. **Insulin:**\n",
        "   - 2-Hour serum insulin (mu U/ml)\n",
        "\n",
        "6. **BMI:**\n",
        "   - Body mass index (weight in kg/(height in m)^2)\n",
        "\n",
        "7. **DiabetesPedigreeFunction:**\n",
        "   - Diabetes pedigree function\n",
        "\n",
        "8. **Age:**\n",
        "   - Age in years"
      ],
      "metadata": {
        "id": "JEl_w-DPaDcF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Target Variable**"
      ],
      "metadata": {
        "id": "j_4YP1AkaSFz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Outcome:**\n",
        "   - Class variable (0 or 1)\n",
        "   - 268 instances are labeled as 1 (indicating diabetes), while others are labeled as 0.\n",
        "\n",
        "This dataset provides a valuable opportunity to develop a predictive model for diabetes based on demographic and health-related features."
      ],
      "metadata": {
        "id": "V0NBAI1CaXhA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the required Library Packages:"
      ],
      "metadata": {
        "id": "IbN-gOy9aj5i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score,mean_squared_error,classification_report,confusion_matrix,precision_score,recall_score,roc_curve,auc\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.ensemble import ExtraTreesClassifier,RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "3Hq3PxmUZpi2"
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      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading and exploring the Health Care **Dataset**"
      ],
      "metadata": {
        "id": "n6YFFnHWa0lC"
      }
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          "text": [
            "Saving health care diabetes.csv to health care diabetes (2).csv\n"
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      "source": [
        "df = pd.read_csv(\"health care diabetes.csv\")\n",
        "df"
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              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
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              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
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              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploring the header of the dataset\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2AEX6hWAdAhY",
        "outputId": "54cf947f-b82d-44b8-d5bf-05c8af289563"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ],
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              "      <th>4</th>\n",
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              "        const element = document.querySelector('#df-92091d31-30ac-443b-b2b3-be7c9b111df8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d150ffc8-cfc8-4c7d-9c0d-bdf83e5ec6c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d150ffc8-cfc8-4c7d-9c0d-bdf83e5ec6c9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-d150ffc8-cfc8-4c7d-9c0d-bdf83e5ec6c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the number of rows and columns of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WIEWqieYdFXh",
        "outputId": "2c21418a-d9cc-419b-dc7d-ae3c2ef0491f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for the presence of null-values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "gVJ5sClIdM9p",
        "outputId": "3f22c2d8-6a0d-4e08-e49d-beca2b48f3b2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pregnancies</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Information Overview\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eufzPz7rdHUy",
        "outputId": "14a0b129-4d3c-448e-9f27-dccb83e6bd46"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary Statistics for the Diabetes Dataset\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "H7FUH0s6dKkY",
        "outputId": "a9d76abc-79ee-4309-fe7f-e8c223275f16"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dad22a49-dec6-43ac-b072-6a332d95bdf0\" 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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dad22a49-dec6-43ac-b072-6a332d95bdf0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.73802348295857,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89453125,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.85250535810619,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.10546875,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.7684730531098,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          20.536458333333332,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350.26059167945886,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.79947916666667,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.76023154067868,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.4769513772427971\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Data Preprocessing: Treating the Missing Values"
      ],
      "metadata": {
        "id": "BDgVxCwUddN0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In this datasets 0 represents the null values, and hence we will replace 0 by mean of their feature (variable) columns."
      ],
      "metadata": {
        "id": "a2ld_u_Hdgwt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Identifying the mean of the features\n",
        "print(df['Glucose'].mean())\n",
        "print(df['BloodPressure'].mean())\n",
        "print(df['SkinThickness'].mean())\n",
        "print(df['Insulin'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLjThn-fdYRB",
        "outputId": "9686f9bf-9ce1-43df-8c63-3339737a53a4"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "120.89453125\n",
            "69.10546875\n",
            "20.536458333333332\n",
            "79.79947916666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the number of rows which has the null values\n",
        "print('Glucose-',len(df['Glucose'][df['Glucose']==0]))\n",
        "print('BloodPressure-',len(df['BloodPressure'][df['BloodPressure']==0]))\n",
        "print('SkinThickness-',len(df['SkinThickness'][df['SkinThickness']==0]))\n",
        "print('Insulin-',len(df['Insulin'][df['Insulin']==0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqrQPu_SdpkK",
        "outputId": "4f5e797f-97a4-4c8e-f786-58e7e2db5e19"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Glucose- 5\n",
            "BloodPressure- 35\n",
            "SkinThickness- 227\n",
            "Insulin- 374\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the null value percentage\n",
        "selected_columns = ['Glucose', 'BloodPressure', 'SkinThickness','Insulin']\n",
        "null_percentage = (df[selected_columns] == 0).mean() * 100\n",
        "\n",
        "# Displaying the null value percentage for each selected column\n",
        "print(\"Percentage of Null Values for Each Column:\")\n",
        "print(null_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwTtRESudztp",
        "outputId": "0ef642eb-3e54-4aad-b790-9175f7eeb1f8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of Null Values for Each Column:\n",
            "Glucose           0.651042\n",
            "BloodPressure     4.557292\n",
            "SkinThickness    29.557292\n",
            "Insulin          48.697917\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inferences from Null Value Percentage Analysis\n",
        "\n",
        "The analysis of null value percentages in the dataset reveals the following insights:\n",
        "\n",
        "1. **Glucose:**\n",
        "   - Approximately 0.65% of the data points in the \"Glucose\" column are represented as null values.\n",
        "\n",
        "2. **Blood Pressure:**\n",
        "   - The \"Blood Pressure\" column has a null value percentage of approximately 4.56%.\n",
        "\n",
        "3. **Skin Thickness:**\n",
        "   - A significant portion of the \"Skin Thickness\" column, around 29.56%, contains null values.\n",
        "\n",
        "4. **Insulin:**\n",
        "   - The \"Insulin\" column exhibits a higher null value percentage, with approximately 48.70% of the data points being null.\n",
        "\n",
        "These findings suggest that imputation or other strategies may be necessary for columns with substantial null values, such as \"Skin Thickness\" and \"Insulin,\" to ensure the integrity of the dataset for subsequent analyses or modeling."
      ],
      "metadata": {
        "id": "6EoGFwPreAN0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Replacing the null values with the mean\n",
        "df['Glucose']=df['Glucose'].replace([0],[df['Glucose'].mean()])\n",
        "df['BloodPressure']=data['BloodPressure'].replace([0],[df['BloodPressure'].mean()])\n",
        "df['SkinThickness']=df['SkinThickness'].replace([0],[df['SkinThickness'].mean()])\n",
        "df['Insulin']=df['Insulin'].replace([0],[df['Insulin'].mean()])"
      ],
      "metadata": {
        "id": "aZHecDOLd243"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the null value percentage of the treated columns\n",
        "null_percentage_treated = (df[selected_columns] == 0).mean() * 100\n",
        "\n",
        "# Displaying the null value percentage for each selected column\n",
        "print(\"Percentage of Null Values for Each Column after the null value treatment:\")\n",
        "print(null_percentage_treated)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLNY1ya7eka6",
        "outputId": "e1dd65e7-2450-4738-d65b-d8020d07e98a"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of Null Values for Each Column after the null value treatment:\n",
            "Glucose          0.0\n",
            "BloodPressure    0.0\n",
            "SkinThickness    0.0\n",
            "Insulin          0.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference from Null Value Treatment\n",
        "\n",
        "After addressing null values in the dataset, it is observed that all selected columns (\"Glucose,\" \"Blood Pressure,\" \"Skin Thickness,\" and \"Insulin\") no longer contain any null values. The null value treatment has been successful, resulting in a clean dataset with 0% null values in these specific columns.\n",
        "*italicized text*"
      ],
      "metadata": {
        "id": "HyROnwEmfERF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Detecting Outliers and Treatment"
      ],
      "metadata": {
        "id": "me_oWz6VfH_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns = df[selected_columns]\n",
        "columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Xd4Oi3t1e-2o",
        "outputId": "3a61a938-2997-4b88-f246-628e73327a0f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Glucose  BloodPressure  SkinThickness     Insulin\n",
              "0      148.0           72.0      35.000000   79.799479\n",
              "1       85.0           66.0      29.000000   79.799479\n",
              "2      183.0           64.0      20.536458   79.799479\n",
              "3       89.0           66.0      23.000000   94.000000\n",
              "4      137.0           40.0      35.000000  168.000000\n",
              "..       ...            ...            ...         ...\n",
              "763    101.0           76.0      48.000000  180.000000\n",
              "764    122.0           70.0      27.000000   79.799479\n",
              "765    121.0           72.0      23.000000  112.000000\n",
              "766    126.0           60.0      20.536458   79.799479\n",
              "767     93.0           70.0      31.000000   79.799479\n",
              "\n",
              "[768 rows x 4 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>89.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>94.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>137.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>168.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>101.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>180.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>122.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>121.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>112.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>126.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>93.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f50dfbe-aadf-4bbf-9edc-b2adda46f23d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      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-9f50dfbe-aadf-4bbf-9edc-b2adda46f23d 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-9f50dfbe-aadf-4bbf-9edc-b2adda46f23d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42d318e4-60d2-4562-beb5-8eb9daa93475')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    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",
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              "    20% {\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-42d318e4-60d2-4562-beb5-8eb9daa93475 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2653f959-3285-47bf-a509-94e03230e405\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('columns')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2653f959-3285-47bf-a509-94e03230e405 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('columns');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "columns",
              "summary": "{\n  \"name\": \"columns\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.436015643897136,\n        \"min\": 44.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151.0,\n          101.0,\n          112.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.115931598253855,\n        \"min\": 24.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86.0,\n          46.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.631240710783171,\n        \"min\": 7.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7.0,\n          12.0,\n          48.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93.08035765165374,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52.0,\n          41.0,\n          183.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display boxplots for numeric columns to visualize outliers\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(data=columns)\n",
        "plt.title(\"Boxplots for Numeric Columns\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "4ydJk8csfMYf",
        "outputId": "1c465b74-29aa-4f7c-dfe4-483a57fff8f1"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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7u8E5EgfLzc3VsmXL1L9/f2VmZqpVq1Z67rnnVFtbq5kzZ7rrzj33XJnNZj388MOqrq5WWFiY/vznP6uoqEizZs3SlVdeqc6dO2vnzp2aPXu2oqOjPfrz1jnnnKMxY8bo+eefV1VVldLT0/X5559r7ty5Gj58uC688EKfn7MpZrNZ9913n/vcmoOlpKSoT58+mjRpkrZv36727dvr1Vdfdc/kHUsPPfSQPvzwQ/Xu3Vvjx49X9+7dtX37dn311Vd6//33tX37dp+f02QyqaCgQMOGDdO5556rG2+8URaLRd9//73Wrl3rPpyyMe3atdNbb72loUOH6txzz9WoUaPUo0cPSdJXX32lV155RWlpaZIOzFxNmjRJ06ZN0yWXXKLLL79c69at06xZs3T++ed7LPN+qJiYGF199dV6+umnFRISos6dO2vRokUNznFqTnPnzj2m+zQAuPlljTkAOA4aW+o6PDzcOPfcc42CgoIGSwvv3LnTuPPOO43ExESjdevWxhlnnGE88sgj7rovv/zSaNWqlcfy1YZxYPni888/30hMTHQvX1y/hPOGDRuMQYMGGZGRkUZ8fLwxZcoUj6WHDaPhUteGYRhfffWVMXjwYKNNmzZGZGSkceGFFxrLly9v8B5nz55tWK1Ww2w2u5cn/uqrr4yRI0cap556qhEWFmZ07NjRuOyyy4wvvvjiiN+zxpa6NgzD2LdvnzFt2jQjOTnZaN26tdGpUydj0qRJxt69ez3qkpKSjEsvvfSIr+PN63Xu3LnBUteGYRgbNmwwBg4caISFhRnx8fFGdna2sWzZskaXuk5JSWnw3IfrsbHXqqioMLKysoxOnToZrVu3NhISEoyLLrrIeP7559019UtDv/766w2e83DLRn/66afGxRdfbLRt29aIiooybDab8fTTTzf6PTrU5s2bjTvvvNM488wzjfDwcCMyMtLo0aOH8eCDDxrV1dUetc8884zRtWtXo3Xr1kZ8fLxxyy23NFhi+9Clrg3DMLZt22aMGDHCiIyMNNq1a2f8z//8j1FaWtroUteN/fy8/d7Xf0YPXcL60O/bH9mnAaApIYZxDK8uBwBB6oYbbtAbb7zBydgAAAQwzvkBAAAAEBQIPwAAAACCAuEHAAAAQFDgnB8AAAAAQYGZHwAAAABBgfADAAAAICi0yIuculwubd68WW3btlVISIi/2wEAAADgJ4ZhaOfOnUpMTJTJ1PTcTosMP5s3b1anTp383QYAAACAALFp0yadcsopTda0yPDTtm1bSQfeYHR0tJ+7AQAAAOAvNTU16tSpkzsjNKVFhp/6Q92io6MJPwAAAAC8Oh2GBQ8AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFAg/AAAAAIJCK383AAAAALRETqdTdrtdlZWViouLk81mk9ls9ndbaALhBwAAAPBRcXGx8vPz5XA43NsSEhKUlZWl9PR0P3aGpnDYGwAAAOCD4uJi5eTkyGq1qqCgQEuWLFFBQYGsVqtycnJUXFzs7xZxGCGGYRj+bsJXNTU1iomJUXV1taKjo/3dDgAAAIKE0+nUyJEjZbValZeXJ5Pp97kEl8ul7OxslZeXq6ioiEPgmokv2YCZHwAAAMBLdrtdDodDo0eP9gg+kmQymTRq1Cht2bJFdrvdTx2iKYQfAAAAwEuVlZWSpOTk5EbHrVarRx0CC+EHAAAA8FJcXJwkqby8vNHxsrIyjzoEFsIPAAAA4CWbzaaEhAQVFhbK5XJ5jLlcLs2bN08Wi0U2m81PHaIphB8AAADAS2azWVlZWSopKVF2drZKS0u1e/dulZaWKjs7WyUlJcrMzGSxgwDFam8AAACAjxq7zo/FYlFmZibX+WlmvmQDwg8AAABwFJxOp+x2uyorKxUXFyebzcaMjx/4kg1aNVNPAAAAwAnFbDYrNTXV323AB5zzAwAAACAoEH4AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFHwKP06nU/fff7+Sk5MVERGhzp0764EHHpBhGO4awzCUk5Mji8WiiIgIDRw4UOvXr/d4nu3btysjI0PR0dGKjY3V2LFjtWvXrmPzjgAAAACgET6Fn4cfflgFBQV65pln9N133+nhhx/WzJkz9fTTT7trZs6cqaeeekrPPvusVq5cqaioKA0ePFh79+5112RkZGjt2rVatmyZFi1apI8//lg333zzsXtXAAAAAHCIEOPgaZsjuOyyyxQfH68XXnjBvW3EiBGKiIjQvHnzZBiGEhMTddddd+nuu++WJFVXVys+Pl5z5szRddddp++++07du3fXqlWr1LNnT0nSkiVLNHToUP3yyy9KTEw8Yh81NTWKiYlRdXW1oqOjfX3PAAAAAE4QvmQDn2Z++vbtqw8++EA//PCDJOnrr7/Wp59+qiFDhkiSysvL5XA4NHDgQPdjYmJi1Lt3b5WUlEiSSkpKFBsb6w4+kjRw4ECZTCatXLmy0detra1VTU2NxxcAAAAA+KKVL8X33nuvampq1LVrV5nNZjmdTj344IPKyMiQJDkcDklSfHy8x+Pi4+PdYw6HQx07dvRsolUrtW/f3l1zqBkzZmjatGm+tAoAAAAAHnya+fnf//1fvfzyyyoqKtJXX32luXPn6tFHH9XcuXOPV3+SpEmTJqm6utr9tWnTpuP6egAAAABOPD7N/EycOFH33nuvrrvuOknS2WefrY0bN2rGjBkaM2aMEhISJEkVFRWyWCzux1VUVOjcc8+VJCUkJGjr1q0ez7t//35t377d/fhDhYWFKSwszJdWAQAAAMCDTzM/u3fvlsnk+RCz2SyXyyVJSk5OVkJCgj744AP3eE1NjVauXKm0tDRJUlpamqqqqvTll1+6a/7zn//I5XKpd+/eR/1GAAAAAKApPs38DBs2TA8++KBOPfVUpaSkaPXq1Xr88cd10003SZJCQkJ0xx13KDc3V2eccYaSk5N1//33KzExUcOHD5ckdevWTZdcconGjx+vZ599Vvv27dOtt96q6667zquV3gAAAADgaPgUfp5++mndf//9yszM1NatW5WYmKj/+Z//UU5Ojrvmnnvu0W+//aabb75ZVVVV6t+/v5YsWaLw8HB3zcsvv6xbb71VF110kUwmk0aMGKGnnnrq2L0rAAAAADiET9f5CRRc5wcAAACAdByv8wMAAAAALRXhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFAg/AAAAAIIC4QcAAABAUCD8AAAAAAgKhB8AAAAAQYHwAwAAACAoEH4AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAgkIrfzcAAAAAtEROp1N2u12VlZWKi4uTzWaT2Wz2d1toAuEHAAAA8FFxcbHy8/PlcDjc2xISEpSVlaX09HQ/doamcNgbAAAA4IPi4mLl5OTIarWqoKBAS5YsUUFBgaxWq3JyclRcXOzvFnEYIYZhGP5uwlc1NTWKiYlRdXW1oqOj/d0OAAAAgoTT6dTIkSNltVqVl5cnk+n3uQSXy6Xs7GyVl5erqKiIQ+CaiS/ZgJkfAAAAwEt2u10Oh0OjR4/2CD6SZDKZNGrUKG3ZskV2u91PHaIphB8AAADAS5WVlZKk5OTkRsetVqtHHQIL4QcAAADwUlxcnCSpvLy80fGysjKPOgQWwg8AAADgJZvNpoSEBBUWFsrlcnmMuVwuzZs3TxaLRTabzU8doimEHwAAAMBLZrNZWVlZKikpUXZ2tkpLS7V7926VlpYqOztbJSUlyszMZLGDAMVqbwAAAICPGrvOj8ViUWZmJtf5aWa+ZAPCDwAAAHAUnE6n7Ha7KisrFRcXJ5vNxoyPH/iSDVo1U08AAADACcVsNis1NdXfbcAHnPMDAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFAg/AAAAAIIC4QcAAABAUCD8AAAAAAgKhB8AAAAAQYHwAwAAACAoEH4AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFAg/AAAAAIIC4QcAAABAUCD8AAAAAAgKrfzdAAAAANASOZ1O2e12VVZWKi4uTjabTWaz2d9toQmEHwAAAMBHxcXFys/Pl8PhcG9LSEhQVlaW0tPT/dgZmsJhbwAAAIAPiouLlZOTI6vVqoKCAi1ZskQFBQWyWq3KyclRcXGxv1vEYYQYhmH4uwlf1dTUKCYmRtXV1YqOjvZ3OwAAAAgSTqdTI0eOlNVqVV5enkym3+cSXC6XsrOzVV5erqKiIg6Baya+ZANmfgAAAAAv2e12ORwOjR492iP4SJLJZNKoUaO0ZcsW2e12P3WIphB+AAAAAC9VVlZKkpKTkxsdt1qtHnUILIQfAAAAwEtxcXGSpPLy8kbHy8rKPOoQWAg/AAAAgJdsNpsSEhJUWFgol8vlMeZyuTRv3jxZLBbZbDY/dYimEH4AAAAAL5nNZmVlZamkpETZ2dkqLS3V7t27VVpaquzsbJWUlCgzM5PFDgIUq70BAAAAPmrsOj8Wi0WZmZlc56eZ+ZINCD8AAADAUXA6nbLb7aqsrFRcXJxsNhszPn7gSzZo1Uw9AQAAACcUs9ms1NRUf7cBH3DODwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFAg/AAAAAIIC4QcAAABAUCD8AAAAAAgKhB8AAAAAQYHwAwAAACAoEH4AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKDQyt8NAAAAAC2R0+mU3W5XZWWl4uLiZLPZZDab/d0WmuDzzM+vv/6qUaNGKS4uThERETr77LP1xRdfuMcNw1BOTo4sFosiIiI0cOBArV+/3uM5tm/froyMDEVHRys2NlZjx47Vrl27/vi7AQAAAJpBcXGxRo4cqb/97W+aPn26/va3v2nkyJEqLi72d2togk/hZ8eOHerXr59at26txYsX69tvv9Vjjz2mdu3auWtmzpypp556Ss8++6xWrlypqKgoDR48WHv37nXXZGRkaO3atVq2bJkWLVqkjz/+WDfffPOxe1cAAADAcVJcXKycnBxZrVYVFBRoyZIlKigokNVqVU5ODgEogIUYhmF4W3zvvffqs88+0yeffNLouGEYSkxM1F133aW7775bklRdXa34+HjNmTNH1113nb777jt1795dq1atUs+ePSVJS5Ys0dChQ/XLL78oMTHxiH3U1NQoJiZG1dXVio6O9rZ9AAAA4A9xOp0aOXKkrFar8vLyZDL9PpfgcrmUnZ2t8vJyFRUVcQhcM/ElG/g087Nw4UL17NlTV199tTp27KjU1FTNnj3bPV5eXi6Hw6GBAwe6t8XExKh3794qKSmRJJWUlCg2NtYdfCRp4MCBMplMWrlyZaOvW1tbq5qaGo8vAAAAoLnZ7XY5HA6NHj3aI/hIkslk0qhRo7RlyxbZ7XY/dYim+BR+ysrKVFBQoDPOOEPvvfeebrnlFt1+++2aO3euJMnhcEiS4uPjPR4XHx/vHnM4HOrYsaPHeKtWrdS+fXt3zaFmzJihmJgY91enTp18aRsAAAA4JiorKyVJycnJjY5brVaPOgQWn8KPy+XSeeedp7y8PKWmpurmm2/W+PHj9eyzzx6v/iRJkyZNUnV1tftr06ZNx/X1AAAAgMbExcVJOnDEU2PKyso86hBYfAo/FotF3bt399jWrVs3/fzzz5KkhIQESVJFRYVHTUVFhXssISFBW7du9Rjfv3+/tm/f7q45VFhYmKKjoz2+AAAAgOZms9mUkJCgwsJCuVwujzGXy6V58+bJYrHIZrP5qUM0xafw069fP61bt85j2w8//KCkpCRJB6b/EhIS9MEHH7jHa2pqtHLlSqWlpUmS0tLSVFVVpS+//NJd85///Ecul0u9e/c+6jcCAAAAHG9ms1lZWVkqKSlRdna2SktLtXv3bpWWlio7O1slJSXKzMxksYMA5dNqb6tWrVLfvn01bdo0XXPNNfr88881fvx4Pf/888rIyJAkPfzww3rooYc0d+5cJScn6/7775fdbte3336r8PBwSdKQIUNUUVGhZ599Vvv27dONN96onj17qqioyKs+WO0NAAAA/lRcXKz8/HyPc9YtFosyMzOVnp7ux86Cjy/ZwKfwI0mLFi3SpEmTtH79eiUnJ2vChAkaP368e9wwDE2ZMkXPP/+8qqqq1L9/f82aNUtnnnmmu2b79u269dZb9fbbb8tkMmnEiBF66qmn1KZNm2P+BgEAAIDjwel0ym63q7KyUnFxcbLZbMz4+MFxDT+BgPADAAAAQDqO1/kBAAAAgJaK8AMAAAAgKLTydwMAAABAS8Q5Py0P4QcAAADwUWOrvSUkJCgrK4vV3gIYh70BAAAAPiguLlZOTo6sVqsKCgq0ZMkSFRQUyGq1KicnR8XFxf5uEYfBam8AAACAl5xOp0aOHCmr1aq8vDyZTL/PJbhcLmVnZ6u8vFxFRUUcAtdMWO0NAAAAOA7sdrscDodGjx7tEXwkyWQyadSoUdqyZYvsdrufOkRTCD8AAACAlyorKyVJycnJjY5brVaPOgQWwg8AAADgpbi4OElSeXl5o+NlZWUedQgshB8AAADASzabTQkJCSosLJTL5fIYc7lcmjdvniwWi2w2m586RFMIPwAAAICXzGazsrKyVFJSouzsbJWWlmr37t0qLS1Vdna2SkpKlJmZyWIHAYrV3gAAAAAfNXadH4vFoszMTK7z08x8yQaEHwAAAOAoOJ1O2e12VVZWKi4uTjabjRkfP/AlG7Rqpp4AAACAE4rZbFZqaqq/24APOOcHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBoZW/GwAAAABaIqfTKbvdrsrKSsXFxclms8lsNvu7LTSB8AMAAAD4qLi4WPn5+XI4HO5tCQkJysrKUnp6uh87Q1MIPwAAAIAPiouLlZOTo7S0NI0cOVJhYWGqra3VypUrlZOTo+nTpxOAAlSIYRiGv5vwVU1NjWJiYlRdXa3o6Gh/twMAAIAg4XQ6NXLkSMXExKiqqkoVFRXusfj4eMXGxqqmpkZFRUUcAtdMfMkGzPwAAAAAXrLb7XI4HHI4HEpLS9P111+v0NBQ1dXVacWKFSopKXHXpaam+rlbHIrwAwAAAHhp27ZtkqQzzjhD5eXl7rAjHTjn54wzztD69evddQgsLHUNAAAAeKmqqkqStH79elmtVhUUFGjJkiUqKCiQ1WrV+vXrPeoQWAg/AAAAgJfqzymJjY1Vbm6uUlJSFBkZqZSUFOXm5io2NtajDoGF8AMAAAB4qaamRtKBmZ3JkyertLRUu3fvVmlpqSZPnuye8amvQ2DhnB8AAADAS/UzO2eccYbKysqUmZnpHrNYLO5zfurrEFgIPwAAAICXOnToIOnAOT9paWm67rrrPK7zU78AQn0dAgvhBwAAAPCSzWZTQkKCYmJiVFZW1mC1ty5duqimpkY2m82PXeJwCD8AAACAl8xms7KyspSTk6O0tDSNHDnSPfPz+eefq6SkRNOnT+cCpwEqxDAMw99N+MqXq7gCAAAAx1pxcbHy8/PlcDjc2ywWizIzM5Wenu7HzoKPL9mA8AMAAAAcBafTKbvdrsrKSsXFxclmszHj4we+ZAMOewMAAACOgtlsVmpqqr/bgA+4zg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBS5yCgAAABwFp9Mpu92uyspKxcXFyWazyWw2+7stNIHwAwAAAPiouLhY+fn5cjgc7m0JCQnKyspSenq6HztDUzjsDQAAAPBBcXGxcnJyZLVaVVBQoCVLlqigoEBWq1U5OTkqLi72d4s4jBDDMAx/N+GrmpoaxcTEqLq6WtHR0f5uBwAAAEHC6XRq5MiRslqtysvLk8n0+1yCy+VSdna2ysvLVVRUxCFwzcSXbMDMDwAAAOAlu90uh8Oh0aNHewQfSTKZTBo1apS2bNkiu93upw7RFMIPAAAA4KXKykpJUnJycqPjVqvVow6BhfADAAAAeCkuLk6SVF5e3uh4WVmZRx0CC+EHAAAA8JLNZlNCQoIKCwvlcrk8xlwul+bNmyeLxSKbzeanDtEUwg8AAADgJbPZrKysLJWUlCg7O1ulpaXavXu3SktLlZ2drZKSEmVmZrLYQYBitTcAAADAR41d58disSgzM5Pr/DQzX7IBFzkFAAAAfJSenq5evXqpoKBAv/76q04++WTdcsstioiI8HdraAIzPwAAAICPZs2apddff11Op9O9zWw26+qrr1ZmZqYfOws+zPwAAAAAx8msWbP06quvql27dho3bpz69u2r5cuX65///KdeffVVSSIABShmfgAAAAAv1dXVafDgwYqOjtb8+fPVqtXvcwn79+/XiBEjVFNTo/fee0+hoaF+7DR4+JINWO0NAAAA8NKCBQvkdDo1btw4j+AjSa1atdLYsWPldDq1YMEC/zSIJhF+AAAAAC9t3rxZktS3b99Gx+u319chsBB+AAAAAC8lJiZKkpYvXy6n06nVq1fr/fff1+rVq+V0OrV8+XKPOgQWzvkBAAAAvFR/zk94eLjatGmjiooK91h8fLx27dqlvXv3cs5PM+KcHwAAAOA4CA0NVZ8+ffTbb79p+/btuv7661VUVKTrr79e27dv12+//aY+ffoQfAIUS10DAAAAXnI6ndqwYYMSExPlcDhUVFSkoqIiSQeu85OYmKiysjI5nU6ZzWY/d4tDMfMDAAAAeMlut8vhcOj+++/XokWL1K9fP1mtVvXr109vv/22Jk+erC1btshut/u7VTSCmR8AAADAS5WVlZKkDz74QG+99ZacTqckqaysTMOGDdOVV17pUYfAwswPAAAA4KW4uDhJ0htvvKFD1w0zDENvvPGGRx0CCzM/AAAAgJe6du3qvt2rVy+NGTNGycnJKi8v19y5c7VixYoGdQgczPwAAAAAXlq4cKHH/XXr1unDDz/UunXrmqxDYGDmBwAAAPBS/UIGf/rTn7R8+XL3TI90YLW3/v3769NPP5Xdbte1117rrzZxGIQfAAAAwEsRERGSpE8++UR9+/ZV7969FRYWptraWq1cuVKffvqpRx0CC+EHAAAA8NLFF1+spUuXymw2a/r06R4XM7300ks1ePBgOZ1OXXzxxX7sEofDOT8AAACAl1q1OjB34HQ6dfXVV2vhwoX673//q4ULF+rqq692L31dX4fAwk8FAAAA8NKOHTs8bj/66KNHrEPgYOYHAAAA8FL99XtuvvlmdejQwWOsY8eOGj9+vEcdAgvhBwAAAPCSzWZTQkKCiouLZTJ5/iodEhKijz/+WBaLRTabzU8doimEHwAAAMBLZrNZAwYM0Lp161RXV6eJEyfqzTff1MSJE1VXV6d169YpPT1dZrPZ362iESGGYRj+bsJXNTU1iomJUXV1taKjo/3dDgAAAIKE0+nUyJEj3b+LOhwO95jFYlF0dLRqampUVFREAGomvmQDFjwAAAAAvGS32+VwODRlyhR17dpVdrtdlZWViouLk81m03fffafMzEzZ7Xalpqb6u10cgvADAAAAeKmyslKSlJycLLPZ3CDgWK1WjzoEFs75AQAAALxUv4pbeXl5o+NlZWUedQgshB8AAADAS/WrvRUWFsrlcnmMuVwuzZs3j9XeAhjhBwAAAPCS2WxWVlaWSkpKlJ2drdLSUu3evVulpaXKzs5WSUmJMjMzWewgQLHaGwAAAOCj4uJi5efnN1jtLTMzU+np6X7sLPj4kg0IPwAAAMBRcDqdDVZ7Y8an+bHUNQAAAHCcNbbaGwIb5/wAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBRY8AAAAAI4Cq721PIQfAAAAwEeNXecnISFBWVlZXOcngHHYGwAAAOCD4uJi5eTkyGq1qqCgQEuWLFFBQYGsVqtycnJUXFzs7xZxGFzkFAAAAPCS0+nUyJEjZbValZeXJ5Pp97kEl8ul7OxslZeXq6ioiEPgmokv2YCZHwAAAMBLdrtdDodDo0eP9gg+kmQymTRq1Cht2bJFdrvdTx2iKYQfAAAAwEuVlZWSpOTk5EbHrVarRx0CC+EHAAAA8FJcXJwkqby8vNHxsrIyjzoEFsIPAAAA4CWbzaaEhAQVFhbK5XJ5jLlcLs2bN08Wi0U2m81PHaIphB8AAADAS2azWVlZWSopKVF2drZKS0u1e/dulZaWKjs7WyUlJcrMzGSxgwDFam8AAACAjxq7zo/FYlFmZibX+WlmvmQDwg8AAABwFJxOp+x2uyorKxUXFyebzcaMjx/4kg1aNVNPAAAAwAnFbDYrNTXV323AB5zzAwAAACAoEH4AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAqu9AQAAAEeBpa5bHsIPAAAA4KPGLnKakJCgrKwsLnIawDjsDQAAAPBBcXGxcnJyZLVaVVBQoCVLlqigoEBWq1U5OTkqLi72d4s4jBDDMAx/N+ErX67iCgAAABwrTqdTI0eOlNVqVV5enkym3+cSXC6XsrOzVV5erqKiIg6Baya+ZANmfgAAAAAv2e12ORwOjR492iP4SJLJZNKoUaO0ZcsW2e12P3WIphB+AAAAAC9VVlZKkpKTkxsdt1qtHnUILIQfAAAAwEtxcXGSpPLy8kbHy8rKPOoQWAg/AAAAgJdsNpsSEhJUWFgol8vlMeZyuTRv3jxZLBbZbDY/dYimEH4AAAAAL5nNZmVlZamkpETZ2dkqLS3V7t27VVpaquzsbJWUlCgzM5PFDgIUq70BAAAAPmrsOj8Wi0WZmZlc56eZsdobAAAAcJwdOodw6GFwCDx/KPw89NBDCgkJ0R133OHetnfvXmVlZSkuLk5t2rTRiBEjVFFR4fG4n3/+WZdeeqkiIyPVsWNHTZw4Ufv37/8jrQAAAADNov4ip507d/a4yGnnzp25yGmAO+rws2rVKj333HMNTua688479fbbb+v1119XcXGxNm/erL/85S/ucafTqUsvvVR1dXVavny55s6dqzlz5ignJ+fo3wUAAADQDJxOp/Lz85WWlqa8vDylpKQoMjJSKSkpysvLU1pammbNmiWn0+nvVtGIowo/u3btUkZGhmbPnq127dq5t1dXV+uFF17Q448/rj//+c/q0aOHXnrpJS1fvlwrVqyQJC1dulTffvut5s2bp3PPPVdDhgzRAw88oPz8fNXV1R2bdwUAAAAcB1zktGU7qvCTlZWlSy+9VAMHDvTY/uWXX2rfvn0e27t27apTTz1VJSUlkqSSkhKdffbZio+Pd9cMHjxYNTU1Wrt2baOvV1tbq5qaGo8vAAAAoLlxkdOWrZWvD3j11Vf11VdfadWqVQ3GHA6HQkNDFRsb67E9Pj7evRKGw+HwCD714/VjjZkxY4amTZvma6sAAADAMXXwRU67du0qu92uyspKxcXFyWazcZHTAOdT+Nm0aZP+9re/admyZQoPDz9ePTUwadIkTZgwwX2/pqZGnTp1arbXBwAAAKTfL3L65JNPqrq62uOP9wkJCYqJieEipwHMp8PevvzyS23dulXnnXeeWrVqpVatWqm4uFhPPfWUWrVqpfj4eNXV1amqqsrjcRUVFUpISJB0YKc4dPW3+vv1NYcKCwtTdHS0xxcAAADQ3MxmswYMGKB169aptrZWEydO1FtvvaWJEyeqtrZW69atU3p6Ohc5DVA+hZ+LLrpI33zzjdasWeP+6tmzpzIyMty3W7durQ8++MD9mHXr1unnn39WWlqaJCktLU3ffPONtm7d6q5ZtmyZoqOj1b1792P0tgAAAIBjz+l06qOPPlKXLl0UGhqqRx55RFdeeaUeeeQRhYWFqUuXLiouLma1twDl02Fvbdu21VlnneWxLSoqSnFxce7tY8eO1YQJE9S+fXtFR0frtttuU1pamvr06SNJGjRokLp3767Ro0dr5syZcjgcmjx5srKyshQWFnaM3hYAAABw7NWv9jZlyhSdccYZWrBggTZv3qzExEQNHz5cP/zwgzIzM2W325WamurvdnEInxc8OJInnnhCJpNJI0aMUG1trQYPHqxZs2a5x81msxYtWqRbbrlFaWlpioqK0pgxYzR9+vRj3QoAAABwTNWv4vbrr79q2rRpHuf8vPHGGxo3bpxHHQJLiGEYhr+b8FVNTY1iYmJUXV3N+T8AAABoNqtXr9bf/vY3SVLfvn01evRoJScnq7y8XIWFhVq+fLkk6R//+AczP83El2xwVNf5AQAAAIJRSkqKzGaz2rVrp9zcXKWkpCgyMlIpKSnKzc1Vu3btZDablZKS4u9W0QjCDwAAAOCltWvXyul0qqqqSpMnT1Zpaal2796t0tJSTZ48WVVVVXI6nVq7dq2/W0UjCD8AAACAl+rP5bnvvvtUVlamzMxMXXLJJcrMzFR5ebnuu+8+jzoElmO+4AEAAABwooqLi5MknXzyyXrllVdkt9tVWVmpuLg42Ww2fffddx51CCzM/AAAAABestlsSkhIUGFhoUJCQpSamqqBAwcqNTVVISEhmjdvniwWi2w2m79bRSOY+QEAAAC8ZDablZWVpZycHGVnZ6tXr14KCwtTbW2tPv/8c5WUlGj69Okym83+bhWNIPwAAAAAPkhPT9e1116r119/3b20tXQgGF177bVKT0/3Y3doCuEHAAAA8EFxcbFee+019enTR3369FFoaKjq6uq0YsUKvfbaa0pJSSEABSjO+QEAAAC85HQ6lZ+fr7S0NOXm5uq0005TWFiYTjvtNOXm5iotLU2zZs2S0+n0d6toBDM/AAAAgJfsdrscDocuv/xyZWRkyOFwuMcSEhI0bNgwLV++XHa7XampqX7sFI0h/AAAAABeqr9+z/PPP6++fftqypQpSk5OVnl5uQoLCzV79myPOgQWDnsDAAAAvNSuXTtJ0tlnn628vDylpKQoMjJSKSkpysvL09lnn+1Rh8BC+AEAAAAQFAg/AAAAgJd27NghSSotLVV2drZKS0u1e/duj/sH1yGwcM4PAAAA4KW4uDhJ0vjx47Vw4UJlZma6xywWi8aNG6fZs2e76xBYCD8AAACAl2w2mxISElRaWqp//etfWrhwoTZv3qzExERdfvnlmjp1qiwWi2w2m79bRSNCDMMw/N2Er2pqahQTE6Pq6mpFR0f7ux0AAAAEkeLiYt1///0KCwtTbW2te3v9/QceeICLnDYjX7IB5/wAAAAAPgoJCfFpOwIDh70BAAAAXnI6ncrPz1daWpoeeOABlZaWqrKyUnFxcTrrrLN0//33a9asWerfv7/MZrO/28UhmPkBAAAAvGS32+VwODR69Gi1bt1aqampGjhwoFJTU9W6dWuNGjVKW7Zskd1u93eraAThBwAAAPBSZWWlJCk5ObnRcavV6lGHwEL4AQAAALxUv4R1eXl5o+NlZWUedQgshB8AAADAS/VLXRcWFsrlcnmMuVwuzZs3j6WuAxjhBwAAAPCS2WxWVlaWSkpKlJ2drdLSUu3evVulpaXKzs5WSUmJMjMzWewgQHGdHwAAAMBHxcXFys/Pl8PhcG+zWCzKzMzkGj/NzJdsQPgBAAAAjoLT6ZTdbncvdW2z2Zjx8QNfsgHX+QEAAACOgtlsVmpqqr/bgA8IPwAAAMBRYOan5SH8AAAAAD5q7JyfhIQEZWVlcc5PAGO1NwAAAMAHxcXFysnJkdVqVUFBgZYsWaKCggJZrVbl5OSouLjY3y3iMFjwAAAAAPCS0+nUyJEjZbValZeXJ5Pp97kEl8ul7OxslZeXq6ioiEPgmokv2YCZHwAAAMBLdrtdDodDo0ePlmEYWr16td5//32tXr1ahmFo1KhR2rJli+x2u79bRSM45wcAAADwUmVlpSTp119/1bRp0xqc8zNu3DiPOgQWwg8AAADgpbi4OEnSgw8+qLS0NE2ZMkXJyckqLy9XYWGhHnzwQY86BBYOewMAAAC8lJKSIrPZrNjYWOXm5iolJUWRkZFKSUlRbm6uYmNjZTablZKS4u9W0QhmfgAAAAAvrV27Vk6nUzt27NB9992n3r17KywsTLW1tVq5cqV27NjhruMCqIGH8AMAAAB4qf5cnhEjRuitt95SSUmJe8xsNmvEiBGaP38+5/wEKA57AwAAALxUfy7P/Pnz1aqV5zyC2WzW/PnzPeoQWAg/AAAAgJdSUlLc1/Y59HKZ9fdNJhPn/AQowg8AAADgpW+++UYul0uStG/fPo+x+vsul0vffPNNs/eGIyP8AAAAAF766quvjmkdmhcLHgAAAABe+vXXXyUdOLRt0aJFevfdd7V582YlJiZq6NChuuyyy+Ryudx1CCyEHwAAAMBLmzZtkiRFRUXppptuksPhcI+98cYbioyM1K5du9x1CCyEHwAAAMBLISEhkqSdO3eqVatWuvbaa5WYmKjNmzdr6dKl2rVrl0cdAgvhBwAAAPBS165d9cMPP0iSduzYoddee+2wdQg8LHgAAAAAeKl///7u24fO7hx8/+A6BA7CDwAAAOClmpoa9+3DXefn0DoEDsIPAAAA4KWqqipJUufOnRsdr99eX4fAQvgBAAAAvBQbGytJOumkk7Ro0SL169dPVqtV/fr106JFi3TSSSd51CGwsOABAAAA4KUOHTpIklauXKkRI0aotrZWklRWVuZxv74OgYWZHwAAAMBLNpvtiLM6sbGxstlszdMQfMLMDwAAAHAUUlNT1adPH4WFham2tlYrVqzQihUruMZPACP8AAAAAF6y2+2qqqrSzTffrIULF2rFihXuMYvFovHjx2v27Nmy2+1KTU31Y6doDIe9AQAAAF6qrKyUJHXs2LHBUtcul0vx8fEedQgszPwAAAAAXoqLi5Mk5ebmqm/fvpo6daqSk5NVXl6uwsJC5ebmetQhsDDzAwAAAHgpJSVFZrNZ7dq107Rp01RXV6fly5errq5O06ZNU7t27WQ2m5WSkuLvVtEIZn4AAAAAL61du1ZOp1M7duzQZZdd5l7aWpJ74YP6Os75CTzM/AAAAABe8vZcHs75CUzM/AAAAABeateunSTp7LPP1pNPPqnS0lJVVlYqLi5OZ511lu644w5988037joEFmZ+AAAAgKPgdDq1fv16lZaWav369XI6nf5uCUfAzA8AAADgpR07dkiSvvnmGw0aNMhj7JlnnmlQh8DCzA8AAADgpYOXsA4JCfEYO/g+S10HJsIPAAAA4KWuXbtKksxmszp27Ogx1rFjR5nNZo86BBbCDwAAAOClt99+W9KB833q6uo0ceJEvfXWW5o4caLq6urc5/3U1yGwcM4PAAAA4KVff/1VktS5c2ft2rVLjzzyiHssISFBnTt31oYNG9x1CCzM/AAAAABeMgxDktSnTx+99NJL6tevn6xWq/r166cXX3xRvXv39qhDYGHmBwAAAPBS9+7dtWDBAr322mt6+eWX3dvLyso0dOhQtWrVyl2HwMPMDwAAAOCl+Ph4SdL+/fslSb169VJ+fr569erlsb2+DoGFmR8AAADAS2eccYb7dkhIiD7//HN9/vnnkiSTySSXy9WgDoGDmR8AAADAS88//7z7duvWrT3G6g95O7QOgYPwAwAAAHjp4FXc6urqPMYOvs9qb4GJ8AMAAAB4KTEx0X07NDTUY+zg+wfXIXAQfgAAAAAvpaWluW8fupz1wfcPrkPgIPwAAAAAXiotLXXf3rdvn8fYwfcPrkPgIPwAAAAAXnI4HMe0Ds2L8AMAAAAchabO+UFgIvwAAAAAXjr4vJ6mVns79HwgBAbCDwAAAOClkJCQY1qH5kX4AQAAAI7CwRc1lRpe9BSBh/ADAAAAHIX9+/d73D909TcEHsIPAAAA4KWEhIRjWofmRfgBAAAAvHTOOee4bze12tvBdQgchB8AAADASz/99JP79qGLGhx8/+A6BA7CDwAAAOClLVu2uG+7XC6PsYPvH1yHwEH4AQAAALyUmJgoSTr//PPldDo9xlwul3r27OlRh8ASYrTAKzDV1NQoJiZG1dXVio6O9nc7AAAACBJ1dXUaNGiQXC6XevfurU6dOqmurk6hoaHatGmTVq5cKZPJpKVLlzY4JwjHhy/ZoFWTowAAAADczGazwsPDtXv3bq1atUorV650j5lMBw6qCg8Pl9ls9leLaAKHvQEAAABestvt2r17t6TDn/Oze/du2e32Zu8NR0b4AQAAALy0bds2SVLv3r21ePFiDR8+XOeff76GDx+uxYsXq3fv3h51CCwc9gYAAAB4qaqqStKBi5jeeOONcjgckqRVq1ZpxYoV6tWrl0cdAgvhBwAAAPBSbGysJOnf//63wsLCPMZ27NihhQsXetQhsBB+AAAAAC/FxcW5b0dERGj48OFKTEzU5s2b9d5776m2trZBHQIH4QcAAADwUv21fVq3bq3q6mq99tpr7rGQkBC1bt1a+/bta3ANIAQGwg8AAADgpfpV3Pbt26d27dpp0KBBslgs2rJli5YuXaodO3a46+rP/0HgIPwAAAAAXqpfzvqUU07R/v37PWZ+LBaLTjnlFP3yyy8NlsFGYGCpawAAAMBL0dHRkqSwsDC99NJLHktdv/jii+5FEOrrEFiY+QEAAAC81L59e0nShg0bNGTIEPf2VatWacGCBQ3qEFiY+QEAAAC81KFDh2Nah+ZF+AEAAAC81LVrV0mSyWRSSEiIx1hISIhMJpNHHQIL4QcAAADw0ttvvy1Jh13QoH57fR0CC+EHAAAA8NIvv/zivt26dWuPsYPvH1yHwEH4AQAAAHx08skn6/XXX9dZZ52ljh076qyzztLrr7+uxMREf7eGJrDaGwAAAOClqKgoSdKWLVt0xRVXuLdv3bpVV1xxhfucn/o6BBZmfgAAAAAvmc1mSb+f25OUlKSMjAwlJSV5bK+vQ2Bh5gcAAADwUrdu3Tzub9y4URs3bjxiHQIDMz8AAACAlxYtWuS+HRMTo86dOyspKUmdO3dWTExMo3UIHMz8AAAAAF7avHmz+3Z1dbWqq6uPWIfAwcwPAAAA4KWDFzJo7CKnjdUhcBB+AAAAAC8NGjTIffuNN97Q8OHDdf7552v48OF64403Gq1D4OCwNwAAAMBLZWVl7tsjRoxw3161apUWLFjQaB0CBzM/AAAAAIIC4QcAAADwksVicd8+77zz1L59e4WHh6t9+/Y677zzGq1D4OCwNwAAAMBHJpNJX331lfv+3r17tX37dplMJveFThF4mPkBAAAAvFRRUSFJ7oDTtWtX3XDDDeratavH9vo6BBZmfgAAAAAvnXTSSZJ+X9b6+++/1/fffy/pwGyQYRgyDMNdh8DCzA8AAADgpS1btkiSQkND9eqrryo+Pl4RERGKj4/XK6+8otDQUI86BBZmfgAAAAAvORwOSVJtba2uvfZa9/Y9e/Z43K+vQ2Bh5gcAAADw0sknn3xM69C8CD8AAACAl66//vpjWofmRfgBAAAAvDR9+vRjWofmRfgBAAAAvOTtuTyc8xOYCD8AAACAl0ym3399Tk1NVfv27RUeHq727dsrNTW10ToEDp9+KjNmzND555+vtm3bqmPHjho+fLjWrVvnUbN3715lZWUpLi5Obdq00YgRIxpc5Onnn3/WpZdeqsjISHXs2FETJ07U/v37//i7AQAAAI6j9u3bu2+vXr1a27dv1969e7V9+3atXr260ToEDp/CT3FxsbKysrRixQotW7ZM+/bt06BBg/Tbb7+5a+688069/fbbev3111VcXKzNmzfrL3/5i3vc6XTq0ksvVV1dnZYvX665c+dqzpw5ysnJOXbvCgAAADgOamtrPe63b99ekyZNahB2Dq1DYAgxDMM42gdv27ZNHTt2VHFxsS644AJVV1erQ4cOKioq0lVXXSXpwFVvu3XrppKSEvXp00eLFy/WZZddps2bNys+Pl6S9Oyzz+rvf/+7tm3b5r4wVFNqamoUExOj6upqRUdHH237AAAAgE8mT56sjz/++Ih1F1xwgXJzc5uhI/iSDf7QwYjV1dWSfp/W+/LLL7Vv3z4NHDjQXdO1a1edeuqpKikpkSSVlJTo7LPPdgcfSRo8eLBqamq0du3aRl+ntrZWNTU1Hl8AAABAc6usrHTfjo6OVmJiotq3b6/ExESPX7wPrkPgaHW0D3S5XLrjjjvUr18/nXXWWZIOrGoRGhqq2NhYj9r4+Hj3ihcOh8Mj+NSP1481ZsaMGZo2bdrRttpi7d27Vxs3bvR3Gy1WUlKSwsPD/d0GAAA4gezevdt9u6k/yh9ch8Bx1OEnKytLpaWl+vTTT49lP42aNGmSJkyY4L5fU1OjTp06HffX9beNGzdq/Pjx/m6jxZo9e7a6dOni7zYAAMAJJDExUeXl5V7VIfAcVfi59dZbtWjRIn388cc65ZRT3NsTEhJUV1enqqoqj9mfiooKJSQkuGs+//xzj+erXw2uvuZQYWFhCgsLO5pWW7SkpCTNnj3b3200auPGjcrNzdXkyZOVlJTk73YaFah9AQAA3wTS0TDXXXedPvvsM0lS27ZttXPnTvdYdHS0eybouuuua7Aqsr9wNMzvfAo/hmHotttu01tvvaWPPvpIycnJHuM9evRQ69at9cEHH2jEiBGSpHXr1unnn39WWlqaJCktLU0PPvigtm7dqo4dO0qSli1bpujoaHXv3v1YvKcTRnh4eMDPXCQlJQV8jwAAoGUL1KNhDg4+kjwOgbvtttuau53D4miY3/kUfrKyslRUVKR///vfatu2rfscnZiYGEVERCgmJkZjx47VhAkT1L59e0VHR+u2225TWlqa+vTpI0kaNGiQunfvrtGjR2vmzJlyOByaPHmysrKygnJ2BwAAAE0LxKNhHnzwQf30008Ntp922mm67777mr+hJnA0zO98Cj8FBQWSpAEDBnhsf+mll3TDDTdIkp544gmZTCaNGDFCtbW1Gjx4sGbNmuWuNZvNWrRokW655RalpaUpKipKY8aM0fTp0//YOwEAAMAJKRCPhvnXv/6lXbt2adKkSfr66691zjnnaMaMGWrTpo2/W0MTfD7s7UjCw8OVn5+v/Pz8w9YkJSXp3Xff9eWlAQAAgIDSpk0b3XrrrRo/frxuvfVWgk8L8Ieu8wMAAAAALQXhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFAg/AAAAAIIC4QcAAABAUCD8AAAAAAgKhB8AAAAAQYHwAwAAACAoEH4AAAAABAXCDwAAAICgQPgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBcIPAAAAgKBA+AEAAAAQFFr5u4FAUFFRoaqqKn+30aJs3LjR47/wTmxsrOLj4/3dBgAAQFAK+vBTUVGhjIxRqqur9XcrLVJubq6/W2hRQkPD9PLL8whAAAAAfhD04aeqqkp1dbXa23mAjIhYf7eDE1jInippw0eqqqoi/AAAAPhB0IefekZErFxRJ/m7DZzAOMEOAADAv/h9DAAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICgQfgAAAAAEBVZ7A4Cj4HQ6ZbfbVVlZqbi4ONlsNpnNZn+3BQB/GBd/9w0Xfj96/rj4O+EHAHxUXFys/Px8ORwO97aEhARlZWUpPT3dj50BwB9TUVGhURkZqq2r83crLQ4XfvddWGio5r38crMGIMIPAPiguLhYOTk5SktL05QpU5ScnKzy8nIVFhYqJydH06dPJwABaLGqqqpUW1enqyR18HczOKFtk/RGXV2zX/yd8AMAXnI6ncrPz1daWpry8vJkMh04bTIlJUV5eXnKzs7WrFmz1L9/fw6BA9CidZCUqBB/t4ETmuGXV2XBAwDwkt1ul8Ph0OjRo93Bp57JZNKoUaO0ZcsW2e12P3UIAACawswPAHipsrJSkpScnNzoggdWq9WjDgAABBbCDwB4KS4uTpL05ptvauHChQ0WPBg2bJhHHQAACCyEn/8TsqeKYwBxXIXsqfJ3C/iDbDabYmNj9fzzz6tv374NFjyYPXu2YmNjZbPZ/N0qAABoBOHn/4Rv+MjfLQBoQZxOp1555RXt2rVLbdq0kdPplCSFhHCCMAAAgYrw83/2dh4gIyLW323gBBayp4qQ3cLZ7XZVVVUpKSlJK1eubDCelJSkjRs3ym63KzU11Q8dAgCAphB+/o8REStX1En+bgMnMA6rbPnqFzI43FW867ez4AEAAIGJ38cAwEtt2rQ5pnUAAKB5EX4AwEuffPKJ+3arVq2UkZGhoqIiZWRkqFWrVo3WAQCAwEH4AQAvffnll+7b559/vvr166f27durX79+Ov/88xutAwAAgYNzfgDASzU1NZIki8Wi8vJyZWZmuscsFossFou2bNnirgMAAIGF8AMAXoqNjdWuXbu0ZcsWvffee/r+++9VWVmpuLg4de3aVYMHD3bXASc6p9Mpu93u/gzYbDaZzWZ/twUATSL8AAhoe/fuPezqas3NarXql19+kSQNHTpUF198sfr166fFixdrwoQJHnXr1q3zV5tuSUlJCg8P93cbOAEVFxcrPz9fDofDvS0hIUFZWVlKT0/3Y2cA0DTCD4CAtnHjRo0fP97fbTTgdDq1ZMkSLVmypMHYxx9/rI8//tgPXXmaPXu2unTp4u82cIIpLi5WTk6O0tLSNGXKFCUnJ6u8vFyFhYXKycnR9OnTCUAAAhbh5/+E7Kli9QccVyF7qvzdQouUlJSk2bNn+7sNt/z8fK1Zs+aw4+eee66ysrKar6EmJCUl+bsFnGCcTqfy8/OVlpamvLw8mUwH/uVMSUlRXl6esrOzNWvWLPXv359D4AAEpKAPP7GxsQoNDZM2fOTvVhAEQkPDOB/ER+Hh4QE1e/HUU09p0qRJ+uyzzxqM9evXTzNmzPBDV0DzsNvtcjgcmjJlijv41DOZTBo1apQyMzNlt9uVmprqpy4B4PCCPvzEx8fr5Zfnqaqqyt+ttCgbN25Ubm6uJk+ezF+XfRAbG6v4+Hh/t4E/aMaMGdqzZ49mzJihjz76SAMGDNCkSZMUERHh79aA46qyslKSlJyc3Oi41Wr1qAOAQBP04Uc6EID4hfToJCUlBdRf5YHmEhERoYyMDH300UfKyMgg+CAoxMXFSZLKy8uVkpLSYLysrMyjDgACDae5AAAAr9hsNiUkJKiwsFAul8tjzOVyad68ebJYLLLZbH7qEACaRvgBAABeMZvNysrKUklJibKzs1VaWqrdu3ertLRU2dnZKikpUWZmJosdAAhYHPYGAAC8lp6erunTpys/P1+ZmZnu7RaLhWWuAQQ8wg8AAPBJenq6+vfvL7vdrsrKSsXFxclmszHjAyDgEX4AAIDPzGYzy1kDaHE45wcAAABAUCD8AAAAAAgKhB8AAAAAQYHwAwAAACAoEH4AAAAABAVWewMgSaqoqFBVVZW/22hRNm7c6PFfeCc2Nlbx8fH+bgNAE36QtE2Gv9vACWyHn16X8ANAFRUVGpWRodq6On+30iLl5ub6u4UWJSw0VPNefpkABASg2NhYmU0mfeBy+bsVBAGzyaTY2NhmfU3CDwBVVVWptq5Ot6T8psQop7/bwQls829mFaw9sM8RfoDAEx8fr4Jnn9XPP//s71ZajC1btuiFF17Q2LFjZbFY/N1Oi3Lqqac2+78FhB8AbolRTiVHE34AIJh17dpVXbt29XcbLca6dev0wgsvqE+fPurSpYu/28ERsOABAAAAgKDAzA8At82/8fcQHF/sYwAAfyL8AHArWNvG3y0AAAAcN4QfAG63pOxSYhQr/OD42fybiZB9gnA6nbLb7aqsrFRcXJxsNpvMZrO/2wKAJhF+ALglRrlY8ADAERUXFys/P18Oh8O9LSEhQVlZWUpPT/djZwDQNMIPAADwWnFxsXJycpSWlqaRI0cqLCxMtbW1WrlypXJycjR9+nQCEICARfgB4Lb5Nw5ZwfHFPtayOZ1O5efn68wzz9SGDRu0fPly91h8fLzOPPNMzZo1S/379+cQOAABifADQLGxsQoLDVXBWn93gmAQFhra7Ff0xrFht9vlcDjkcDjUt29fTZ06VcnJySovL1dhYaE7DNntdqWmpvq5WwBoiPADQPHx8Zr38suqqqrydystysaNG5Wbm6vJkycrKSnJ3+20GLGxsc1+RW8cG9u2bZMk9e7dWw888IBKS0u1fPlyxcXF6YEHHlB2drZWrlzprgOAQEP4ASDpQADiF9Kjk5SUxFW9ERTq/0CSkJCg66+/XhUVFe6x+Ph49e7d26MOAAIN4QcAAHil/nDFf//73w3GKioqtHDhQo86AAg0XGobAAB4JS4u7pjWAUBzI/wAAACv7Nu375jWAUBzI/wAAACvLF269JjWAUBzI/wAAACv/Pjjj8e0DgCaGwseAAAAr7hcLvftxYsX64cfflBlZaXi4uJ05plnasiQIQ3qACCQEH4AAAhwe/fu1caNG/3dhvbs2eO+fc899+jSSy/VySefrF9//VX//Oc/PerWrVvnjxYbSEpKUnh4uL/bABAgCD8AAAS4jRs3avz48f5uw8M333yjb775ptGxbdu2BUy/s2fP5jpcANwIPwAABLikpCTNnj3b321o3rx5Ki4uPmJdenq6Ro0a1QwdHVlSUpK/WwAQQAg/AAAEuPDw8ICYvcjOzvYq/GRnZysiIqIZOgIA3xB+AligHOPdmPq+ArU/ieO8cXw5nU73OQ3r1q3T6aefLrPZ7OeugOMrIiJC/fr102effXbYmn79+hF8AASsEMMwDH834auamhrFxMSourpa0dHR/m7nuFm3bl3AHDPdEnGcN46X4uJi5efny+FwuLclJCQoKytL6enpfuwMaB6TJk1qNAD169dPM2bM8ENHgP/U/77G7x3+40s2YOYngAXKMd4tFcd5nxgCbQb0q6++0rPPPiubzaZLLrlEc+bM0Q033KDVq1crJydHf/3rX3Xeeef5u01JzH7i+JkxY4b27NmjGTNm6KOPPtKAAQM0adIkZnwABDzCTwALlGO8AX8KxFWuJOnrr7/W119/LUmaM2eOe3tBQYGfOmqIv0LieIqIiFBGRoY++ugjZWRkEHwAtAiEHwABLZBmQNetW6dHH31U9957rzp37txgfMOGDXrooYd09913B0ToYPYTAABPhB8AAS2QZkA3bdokSRowYIBatWqlBQsWaPPmzUpMTNTw4cPVqVMnPfTQQ4qMjAyYngEAwO8IPwDgpbi4OEnSo48+qg8//FBOp9M9VlBQoAsvvNCjDgAABBbCDwB4yWazKSoqSu+//75iY2M1fvx49e3bV8uXL9fs2bP1/vvvKyoqSjabzd+tAgCARhB+AMBLTqdTe/bskSR169ZNycnJioiIUHJysrp166aSkhLt2bNHTqeTa/60UBUVFaqqqvJ3Gy1GS7jmW6CKjY1VfHy8v9sAgg7hBwC8tGDBArlcLl1xxRVauXKlMjMz3WMWi0WXX365Fi5cqAULFuiaa67xY6c4GhUVFcoYlaG62jp/t9Li5Obm+ruFFic0LFQvz3uZAAQ0M8IPAHhp8+bNkqQuXbpoxYoVHmMul0tdu3bVwoUL3XVoWaqqqlRXWydXL5eM6BZ3/W+0ICE1Iar7vE5VVVWEH6CZEX4AwEuJiYmSpJkzZ6pv376aOnWqkpOTVV5ersLCQs2cOdOjDi2TEW1I7fzdBU5khgjXgL+Y/N0AALQUw4YNkyS1bt1a06dPV0pKiiIjI5WSkqLp06erdevWHnUAACCwEH4AwEvff/+9JGnfvn26+uqrtXDhQv33v//VwoULdfXVV2vfvn0edQAAILBw2BsAeKmyslKSNGLECC1YsECPPvqoe8xsNmvEiBGaP3++uw4tVI2/G8AJj30M8BvCDwB4qf7ipQMHDtQtt9yiBQsWaPPmzUpMTNTw4cP1ww8/aP78+VzktIUzf84y5QBwoiL8AICXbDabEhISVFhYqLy8PI/lrF0ul+bNmyeLxcJFTluo2NhYtQ5trX11+/zdCoJA69DWio2N9XcbQNAh/ACAl8xms7KyspSTk6Ps7GyNGjVKVqtVZWVlmjdvnkpKSjR9+nQucNpCxcfHq+jlIi5y6oONGzcqNzdXkydPVlJSkr/baVG4yCngH4QfAPBBenq6pk+frvz8/AYXOZ0+fbrS09P92B3+qPj4eH4hPQpJSUnq0qWLv9sAgCMi/ACAj9LT09W/f3/Z7XZVVlYqLi5ONpuNGR8AAAIc4QcAjoLZbFZqaqq/2wCAoLB3715t3LjR3200qr6vQO1POjA7Gx4e7u82AgLhBwAAAAFt48aNGj9+vL/baFJubq6/Wzis2bNnc2jq/yH8AAAAIKAlJSVp9uzZ/m6jxWJBkt8RfgAAABDQwsPDmbnAMUH4AQAgwAXq+Q6c6wCgpSH8AAAQ4AL9fAfOdQDQUvg1/OTn5+uRRx6Rw+HQOeeco6efflq9evXyZ0sAAAQcznc4epzrAOBgfgs/r732miZMmKBnn31WvXv31pNPPqnBgwdr3bp16tixo7/aAgAg4HC+AwAcGyZ/vfDjjz+u8ePH68Ybb1T37t317LPPKjIyUi+++KK/WgIAAABwAvNL+Kmrq9OXX36pgQMH/t6IyaSBAweqpKSkQX1tba1qamo8vgAAAADAF34JP//973/ldDoVHx/vsT0+Pl4Oh6NB/YwZMxQTE+P+6tSpU3O1CgAAAOAE4bfD3nwxadIkVVdXu782bdrk75YAAAAAtDB+WfDgpJNOktlsVkVFhcf2iooKJSQkNKgPCwtTWFhYc7UHAAAA4ATkl5mf0NBQ9ejRQx988IF7m8vl0gcffKC0tDR/tAQAAADgBOe3pa4nTJigMWPGqGfPnurVq5eefPJJ/fbbb7rxxhv91RIAAACAE5jfws+1116rbdu2KScnRw6HQ+eee66WLFnSYBEEAAAAADgWQgzDMPzdhK9qamoUExOj6upqRUdH+7sdAAAAAH7iSzZoEau9AQAAAMAfRfgBAAAAEBQIPwAAAACCAuEHAAAAQFAg/AAAAAAICoQfAAAAAEGB8AMAAAAgKBB+AAAAAAQFwg8AAACAoED4AQAAABAUCD8AAAAAggLhBwAAAEBQIPwAAAAACAqEHwAAAABBgfADAAAAICi08ncDR8MwDElSTU2NnzsBAAAA4E/1maA+IzSlRYafnTt3SpI6derk504AAAAABIKdO3cqJiamyZoQw5uIFGBcLpc2b96stm3bKiQkxN/tBKWamhp16tRJmzZtUnR0tL/bAfyCzwGCHZ8BgM9BIDAMQzt37lRiYqJMpqbP6mmRMz8mk0mnnHKKv9uApOjoaD7oCHp8DhDs+AwAfA787UgzPvVY8AAAAABAUCD8AAAAAAgKhB8clbCwME2ZMkVhYWH+bgXwGz4HCHZ8BgA+By1Ni1zwAAAAAAB8xcwPAAAAgKBA+AEAAAAQFAg/AAAAAIIC4SeIhYSEaMGCBf5uA2jSTz/9pJCQEK1Zs+a4vs5HH32kkJAQVVVVHdfXAY7Gkf5/fdppp+nJJ588pq85YMAA3XHHHX+or4NNnTpV55577h/uC2hJDv03jH9r/I/wc4JyOBz629/+ptNPP13h4eGKj49Xv379VFBQoN27d/u7PcDthhtuUEhIiPsrLi5Ol1xyiex2u1/7qv8Hqv4rPj5eI0aMUFlZmV/7wolp27ZtuuWWW3TqqacqLCxMCQkJGjx4sD777DOvHr9q1SrdfPPNXtVOnTrVY99u7MtbW7Zs0ZAhQ7yuBwLBDTfcoOHDh/vltfv27astW7Z4fUFOHHuEnxNQWVmZUlNTtXTpUuXl5Wn16tUqKSnRPffco0WLFun999/3d4uAh0suuURbtmzRli1b9MEHH6hVq1a67LLL/N2WJGndunXavHmzXn/9da1du1bDhg2T0+lsUGcYhvbv3++HDg8vEHtC40aMGKHVq1dr7ty5+uGHH7Rw4UINGDBAlZWVXj2+Q4cOioyM9Kr27rvvdn/etmzZolNOOUXTp0/32OathIQElvcFfBAaGqqEhASf/siAY4vwcwLKzMxUq1at9MUXX+iaa65Rt27dZLVadcUVV+idd97RsGHDGjymsWnYNWvWKCQkRD/99JN722effaYBAwYoMjJS7dq10+DBg7Vjxw5JUm1trW6//XZ17NhR4eHh6t+/v1atWuV+7I4dO5SRkaEOHTooIiJCZ5xxhl566SX3+KZNm3TNNdcoNjZW7du31xVXXOHx2jhx1f+lOyEhQeeee67uvfdebdq0Sdu2bWu0vri4WL169VJYWJgsFovuvfdej1/yj7QvStK7776rM888UxEREbrwwgsPu6917NhRFotFF1xwgXJycvTtt9/qxx9/dH9mFi9erB49eigsLEyffvqpXC6XZsyYoeTkZEVEROicc87RG2+84X6+pj4HdXV1uvXWW2WxWBQeHq6kpCTNmDFDUuOH/1VVVSkkJEQfffSRJB11T/CvqqoqffLJJ3r44Yd14YUXKikpSb169dKkSZN0+eWXN/qYKVOmyGKxuGdIDz3sLSQkRP/85z915ZVXKjIyUmeccYYWLlwoSWrTpo3785aQkCCz2ay2bdt6bKvncrl0zz33qH379kpISNDUqVM9+jj0sLdffvlFI0eOVPv27RUVFaWePXtq5cqVjb6HDRs2yGq16tZbb5VhGJozZ45iY2P13nvvqVu3bmrTpo37DyMH++c//6lu3bopPDxcXbt21axZs9xjTX2GDMPQ1KlT3bNriYmJuv3225v+4eCEN2DAAN1+++2H3c+PtN80duhnbGys5syZ0+jrHfr7lrf7PY4dws8JprKyUkuXLlVWVpaioqIarTnavzasWbNGF110kbp3766SkhJ9+umnHn8Fv+eeezR//nzNnTtXX331lU4//XQNHjxY27dvlyTdf//9+vbbb7V48WJ99913Kigo0EknnSRJ2rdvnwYPHqy2bdvqk08+0Weffeb+H0BdXd1R9YuWadeuXZo3b55OP/10xcXFNRj/9ddfNXToUJ1//vn6+uuvVVBQoBdeeEG5ubnumiPti5s2bdJf/vIXDRs2TGvWrNG4ceN07733HrG3iIgISfLYJ++991499NBD+u6772Sz2TRjxgz961//0rPPPqu1a9fqzjvv1KhRo1RcXCyp6c/BU089pYULF+p///d/tW7dOr388ss67bTTfP4e+toT/KtNmzZq06aNFixYoNra2iZrDcPQbbfdpn/961/65JNPZLPZDls7bdo0XXPNNbLb7Ro6dKgyMjLcnwFvzZ07V1FRUVq5cqVmzpyp6dOna9myZY3W7tq1S+np6fr111+1cOFCff3117rnnnvkcrka1NrtdvXv31/XX3+9nnnmGfe/S7t379ajjz6qwsJCffzxx/r555919913ux/38ssvKycnRw8++KC+++475eXl6f7779fcuXMlNf0Zmj9/vp544gk999xzWr9+vRYsWKCzzz7bp+8HTkxN7efNsd8cab/HMWbghLJixQpDkvHmm296bI+LizOioqKMqKgo45577jEMwzAkGW+99ZZhGIbx4YcfGpKMHTt2uB+zevVqQ5JRXl5uGIZhjBw50ujXr1+jr7tr1y6jdevWxssvv+zeVldXZyQmJhozZ840DMMwhg0bZtx4442NPr6wsNDo0qWL4XK53Ntqa2uNiIgI47333vPpe4CWZcyYMYbZbHbvn5IMi8VifPnll4ZhGEZ5ebkhyVi9erVhGIaRnZ3dYF/Jz8832rRpYzidTq/2xUmTJhndu3f36OPvf/+7x2fg0M/E5s2bjb59+xonn3yyUVtb6x5fsGCB+zn27t1rREZGGsuXL/d47rFjxxojR440DKPpz8Ftt91m/PnPf/Z4b/UO/T4YhmHs2LHDkGR8+OGHHj372hP874033jDatWtnhIeHG3379jUmTZpkfP311+5xScbrr79uXH/99Ua3bt2MX375xePxSUlJxhNPPOFRP3nyZPf9Xbt2GZKMxYsXN3jtQx9bLz093ejfv7/HtvPPP9/4+9//7vE69f+OPPfcc0bbtm2NysrKRt/jlClTjHPOOcf47LPPjHbt2hmPPvqox/hLL71kSDJ+/PFH97b8/HwjPj7efb9z585GUVGRx+MeeOABIy0tzTCMpj9Djz32mHHmmWcadXV1jfaH4DFmzBjjiiuuMAzjyPv5kfabgz8D9WJiYoyXXnrJMIyG/+8+9N8Wb/Z7HFvM/ASJzz//XGvWrFFKSsoR/7J4OPUzP43ZsGGD9u3bp379+rm3tW7dWr169dJ3330nSbrlllv06quv6txzz9U999yj5cuXu2u//vpr/fjjj2rbtq37r6Dt27fX3r17tWHDhqPqFy3HhRdeqDVr1mjNmjX6/PPPNXjwYA0ZMkQbN25sUPvdd98pLS3NYwazX79+2rVrl3755Rev9sXvvvtOvXv39njetLS0Rns75ZRTFBUVpcTERP3222+aP3++QkND3eM9e/Z03/7xxx+1e/duXXzxxe79uE2bNvrXv/7l3o+b+hzccMMNWrNmjbp06aLbb79dS5cu9eXbeNQ9wf9GjBihzZs3a+HChbrkkkv00Ucf6bzzzvM4dObOO+/UypUr9fHHH+vkk08+4nMePCsUFRWl6Ohobd261ae+Dp1Zslgsh32ONWvWKDU1Ve3btz/s8/3888+6+OKLlZOTo7vuuqvBeGRkpDp37tzo6/3222/asGGDxo4d67Ev5+bmuvflpj5DV199tfbs2SOr1arx48frrbfe4pw4SGp6P2+O/aap/R7HXit/N4Bj6/TTT1dISIjWrVvnsd1qtUr6/bCdQ5lMB3KwYRjubfv27fOoOdxjvVX/y+y7776rZcuW6aKLLlJWVpYeffRR7dq1Sz169NDLL7/c4HEdOnT4Q6+LwBcVFaXTTz/dff+f//ynYmJiNHv2bI0bN86PnUmffPKJoqO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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "M8cYnjIRfZcA",
        "outputId": "90b730d0-5f81-401e-b223-7d2c87d65d60"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  121.681605      72.254807      26.606479  118.660163   \n",
              "std       3.369578   30.436016      12.115932       9.631241   93.080358   \n",
              "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
              "25%       1.000000   99.750000      64.000000      20.536458   79.799479   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   79.799479   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b038db60-e989-4190-aaeb-1943f2278739\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>121.681605</td>\n",
              "      <td>72.254807</td>\n",
              "      <td>26.606479</td>\n",
              "      <td>118.660163</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>30.436016</td>\n",
              "      <td>12.115932</td>\n",
              "      <td>9.631241</td>\n",
              "      <td>93.080358</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.750000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 239.510868070369,\n        \"min\": 30.436015643897136,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          121.68160502115886,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 251.28086638098014,\n        \"min\": 12.115931598253855,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          72.25480651855469,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.12880430873116,\n        \"min\": 7.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          26.606479220920136,\n          23.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.3906767836551,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          118.66016303168404,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.76023154067868,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.4769513772427971\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the Outlier Count in the selected Columns:\n",
        "def find_outliers_iqr(data, column_name):\n",
        "    # Calculate the first quartile (Q1) and third quartile (Q3)\n",
        "    Q1 = df[column_name].quantile(0.25)\n",
        "    Q3 = df[column_name].quantile(0.75)\n",
        "\n",
        "    # Calculate the interquartile range (IQR)\n",
        "    IQR = Q3 - Q1\n",
        "\n",
        "    # Define the lower and upper bounds for outliers\n",
        "    lower_bound = Q1 - 1.5 * IQR\n",
        "    upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "    # Find outliers\n",
        "    outliers = df[(df[column_name] < lower_bound) | (df[column_name] > upper_bound)]\n",
        "\n",
        "    # Count the number of outliers\n",
        "    count_outliers = len(outliers)\n",
        "\n",
        "    return count_outliers\n",
        "\n",
        "# Calculate and print the number of outliers for each column of interest\n",
        "for column_name in selected_columns:\n",
        "    outlier_count = find_outliers_iqr(df, column_name)\n",
        "    print(f\"Number of outliers in the '{column_name}' column: {outlier_count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ezt_d79fkx7",
        "outputId": "a7f7636d-052b-4cca-e151-a0bde2a50732"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers in the 'Glucose' column: 0\n",
            "Number of outliers in the 'BloodPressure' column: 14\n",
            "Number of outliers in the 'SkinThickness' column: 12\n",
            "Number of outliers in the 'Insulin' column: 89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Boxplot Analysis for Numerical Columns"
      ],
      "metadata": {
        "id": "LrajvHFdg57m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The boxplot illustrates the distribution of four numerical columns: Glucose, BloodPressure, Skin Thickness, and Insulin. The following inferences can be drawn:\n",
        "\n",
        "#### Glucose\n",
        "- Median glucose level: ~200 mg/dL\n",
        "- IQR is large, indicating considerable variability in glucose levels.\n",
        "- There are no outliers\n",
        "\n",
        "#### Blood Pressure\n",
        "- Median blood pressure: 72 mmHg (within the normal range).\n",
        "- IQR is relatively small, suggesting more consistent blood pressure levels.\n",
        "- Few outliers, none extremely high or low.\n",
        "\n",
        "#### Skin Thickness\n",
        "- Median skin thickness: ~25 mm\n",
        "- IQR is small, indicating less considerable variability in skin thickness.\n",
        "- Few outliers, none extremely high.\n",
        "\n",
        "#### Insulin\n",
        "- Median insulin level: ~79 mIU/L\n",
        "- IQR is large, indicating considerable variability in insulin levels.\n",
        "- More outliers, many are extremely high.\n",
        "\n",
        "#### Overall Observations\n",
        "- All columns exhibit a wide range of values, with some outliers. Insulin column has many outliers\n",
        "- Median values for all columns, except the insulin column fall within the normal range.\n",
        "\n",
        "#### Additional Inferences\n",
        "- Glucose levels show more variability than blood pressure levels.\n",
        "- More outliers in the insulin columns compared to blood pressure and skin thickness.\n",
        "\n",
        "#### Possible Interpretations\n",
        "- Variability in glucose levels may be influenced by factors like diet, exercise, and stress.\n",
        "- Outliers in the Insulin column may also be associated with underlying medical conditions or physiological factors. Elevated insulin levels could be indicative of conditions such as insulin resistance or diabetes. Additionally, factors such as dietary habits, genetic predisposition, or specific medical treatments may contribute to higher insulin levels. Further investigation and domain expertise are necessary to understand the potential health implications of these outliers in the Insulin column.\n",
        "\n",
        "It is essential to note that these inferences are based on a single boxplot, and further information is needed to draw definitive conclusions.\n"
      ],
      "metadata": {
        "id": "djvUN8def2VE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outlier Treatment"
      ],
      "metadata": {
        "id": "0aWbUfURg1Pd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sorted(df)\n",
        "Q1=df.quantile(0.20)\n",
        "Q3=df.quantile(0.80)\n",
        "IQR=Q3-Q1\n",
        "print(IQR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8U9NB4SFfw6j",
        "outputId": "6152f2e2-8254-4bb4-a66a-4cb757fbf130"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pregnancies                  6.000000\n",
            "Glucose                     52.000000\n",
            "BloodPressure               20.000000\n",
            "SkinThickness               14.463542\n",
            "Insulin                     70.200521\n",
            "BMI                         11.900000\n",
            "DiabetesPedigreeFunction     0.467600\n",
            "Age                         19.600000\n",
            "Outcome                      1.000000\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleared_iqr = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "df_cleared_iqr\n",
        "print(df_cleared_iqr.shape)\n",
        "print(df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQFvixDfhAOA",
        "outputId": "c4b4e78e-70af-4bb0-dedc-61894d68221a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(678, 9)\n",
            "(768, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleared_iqr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-rgvSi9JhJNE",
        "outputId": "3706594e-0efe-477c-ba06-5e3ab2946237"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
              "0              6    148.0           72.0      35.000000   79.799479  33.6   \n",
              "1              1     85.0           66.0      29.000000   79.799479  26.6   \n",
              "2              8    183.0           64.0      20.536458   79.799479  23.3   \n",
              "3              1     89.0           66.0      23.000000   94.000000  28.1   \n",
              "5              5    116.0           74.0      20.536458   79.799479  25.6   \n",
              "..           ...      ...            ...            ...         ...   ...   \n",
              "763           10    101.0           76.0      48.000000  180.000000  32.9   \n",
              "764            2    122.0           70.0      27.000000   79.799479  36.8   \n",
              "765            5    121.0           72.0      23.000000  112.000000  26.2   \n",
              "766            1    126.0           60.0      20.536458   79.799479  30.1   \n",
              "767            1     93.0           70.0      31.000000   79.799479  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "5                       0.201   30        0  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[678 rows x 9 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
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              "      <td>183.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>678 rows × 9 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cleared_iqr",
              "summary": "{\n  \"name\": \"df_cleared_iqr\",\n  \"rows\": 678,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 15,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.916520324504738,\n        \"min\": 44.0,\n        \"max\": 197.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          80.0,\n          104.0,\n          147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.506689807495688,\n        \"min\": 38.0,\n        \"max\": 110.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          44.0,\n          76.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.90747141124613,\n        \"min\": 8.0,\n        \"max\": 56.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          28.0,\n          16.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.20836477430349,\n        \"min\": 15.0,\n        \"max\": 255.0,\n        \"num_unique_values\": 134,\n        \"samples\": [\n          159.0,\n          194.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.555824925277319,\n        \"min\": 18.2,\n        \"max\": 53.2,\n        \"num_unique_values\": 235,\n        \"samples\": [\n          46.7,\n          26.3,\n          36.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26791626229859344,\n        \"min\": 0.078,\n        \"max\": 1.353,\n        \"num_unique_values\": 466,\n        \"samples\": [\n          0.396,\n          0.698,\n          0.696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 72,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          44,\n          63,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inferences from Outlier Removal using IQR Method\n",
        "\n",
        "1. **Data Size Reduction:**\n",
        "   - After removing outliers using the interquartile range (IQR) method, the dataset has been reduced from 768 to 678 rows.\n",
        "\n",
        "2. **Outliers Identified:**\n",
        "   - Outliers were detected and removed across various columns, particularly impacting features like Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, and Age.\n",
        "\n",
        "3. **Increased Data Robustness:**\n",
        "   - The IQR-based outlier removal contributes to a more robust dataset, potentially improving the reliability of statistical analyses and modeling.\n",
        "\n",
        "4. **Preserved Features:**\n",
        "   - The operation was applied to 9 columns, including predictors like Glucose and Skin Thickness, as well as the target variable Outcome.\n",
        "\n",
        "5. **Consideration for Domain Knowledge:**\n",
        "   - The decision to remove outliers should be made with consideration for domain knowledge, as outliers may contain valuable information or indicate specific health conditions.\n",
        "\n",
        "**Final Dataset Statistics:**\n",
        "- Dataset size after outlier removal: 678 rows.\n",
        "- Original dataset size: 768 rows.\n"
      ],
      "metadata": {
        "id": "SkSlyiCbhuKj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "col = df_cleared_iqr[[\"Glucose\" , \"BloodPressure\" , \"SkinThickness\" , \"Insulin\"]]\n",
        "col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FroPcrGwhrti",
        "outputId": "6ff1178a-a331-466e-e464-9839d8a61362"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Glucose  BloodPressure  SkinThickness     Insulin\n",
              "0      148.0           72.0      35.000000   79.799479\n",
              "1       85.0           66.0      29.000000   79.799479\n",
              "2      183.0           64.0      20.536458   79.799479\n",
              "3       89.0           66.0      23.000000   94.000000\n",
              "5      116.0           74.0      20.536458   79.799479\n",
              "..       ...            ...            ...         ...\n",
              "763    101.0           76.0      48.000000  180.000000\n",
              "764    122.0           70.0      27.000000   79.799479\n",
              "765    121.0           72.0      23.000000  112.000000\n",
              "766    126.0           60.0      20.536458   79.799479\n",
              "767     93.0           70.0      31.000000   79.799479\n",
              "\n",
              "[678 rows x 4 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>148.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>85.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>183.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>89.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>94.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>116.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>101.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>180.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>122.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>121.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>112.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>126.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>93.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>79.799479</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>678 rows × 4 columns</p>\n",
              "</div>\n",
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              "      const quickchartButtonEl =\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "col",
              "summary": "{\n  \"name\": \"col\",\n  \"rows\": 678,\n  \"fields\": [\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.916520324504738,\n        \"min\": 44.0,\n        \"max\": 197.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          80.0,\n          104.0,\n          147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.506689807495688,\n        \"min\": 38.0,\n        \"max\": 110.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          44.0,\n          76.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.90747141124613,\n        \"min\": 8.0,\n        \"max\": 56.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          28.0,\n          16.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.20836477430349,\n        \"min\": 15.0,\n        \"max\": 255.0,\n        \"num_unique_values\": 134,\n        \"samples\": [\n          159.0,\n          194.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the outliers after treatment using box plot\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(data=col)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "DvaB-1-eh4wh",
        "outputId": "d0490af8-85b1-4e19-a721-17b247f9b168"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### It can be observed that the outliers have been significantly reduced"
      ],
      "metadata": {
        "id": "sCFh4oiyiGcB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "n5FsvwMyqw2l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Scaling"
      ],
      "metadata": {
        "id": "QPybvVQZl8sJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the features in df_cleared_iqr\n",
        "scaler = StandardScaler()\n",
        "X = df_cleared_iqr.drop(\"Outcome\", axis=1)  # Assuming 'Outcome' is the target\n",
        "y = df_cleared_iqr[\"Outcome\"]\n",
        "\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "x67FtLIHjD1t"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train-Test Split"
      ],
      "metadata": {
        "id": "sTIkMyNimI59"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "_eoodctKmCtc"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "vBDFAeFKqyRy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Selection and Initial Training\n",
        "\n"
      ],
      "metadata": {
        "id": "AJtQ7WgEmSNj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Logistic Regression\n",
        "2. Random Forest Classifier\n",
        "3. Support Vector Classifier (SVC)\n",
        "4. K-Nearest Neighbors (KNN)"
      ],
      "metadata": {
        "id": "SHYdO--Yms_E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Logistic Regression\n",
        "\n"
      ],
      "metadata": {
        "id": "u-OINJrSmkOX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "How it Works: Logistic Regression is a linear model used for binary classification. It estimates the probability that a given input point belongs to a certain class (0 or 1) by applying the logistic function to a linear combination of input features. The output is a probability score, which is then mapped to class labels.\n",
        "\n",
        "Why to Choose It: Logistic Regression is simple, interpretable, and performs well on linearly separable data. It’s a good baseline model to start with for binary classification tasks, and it’s computationally efficient, which is beneficial for quicker training and evaluation."
      ],
      "metadata": {
        "id": "bnsRI177nSZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "y_pred_log = log_reg.predict(X_test)\n",
        "accuracy_log = accuracy_score(y_test, y_pred_log)\n",
        "print(\"Logistic Regression Accuracy:\", accuracy_log)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDsb27O0mOVH",
        "outputId": "930b3409-66d3-4250-fe6e-177dfa44c830"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 0.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Random Forest Classifier"
      ],
      "metadata": {
        "id": "upuHP3hfm8qd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "How it Works: Random Forest is an ensemble method that combines multiple decision trees. Each tree is trained on a random subset of the data with a random subset of features, and the final prediction is made by averaging the predictions of each tree. This \"bagging\" approach reduces overfitting and improves model stability.\n",
        "\n",
        "Why to Choose It: Random Forests are robust to overfitting, perform well on nonlinear relationships, and can automatically capture feature importance. They handle noisy data effectively and provide higher accuracy for many structured data problems, making them ideal for healthcare datasets with mixed feature importance."
      ],
      "metadata": {
        "id": "Hyd-a9RWnOjK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf.fit(X_train, y_train)\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "print(\"Random Forest Accuracy:\", accuracy_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ybLcWHU0m7B8",
        "outputId": "e75f9c7e-9603-4591-860a-60b8da103646"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.7529411764705882\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Support Vector Classifier (SVC)"
      ],
      "metadata": {
        "id": "2_Ylj6kunpY0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "How it Works: Support Vector Classifier works by finding a hyperplane in a multidimensional space that best separates the classes. It maximizes the margin between the closest points (support vectors) of the classes. SVC can use kernel functions (like linear, polynomial, or radial basis function) to map non-linear data into a higher-dimensional space for linear separability.\n",
        "\n",
        "Why to Choose It: SVCs are powerful for binary classification, especially when data is not linearly separable in the original space. They’re effective for smaller datasets as they avoid overfitting, and the ability to use kernel functions makes SVC flexible for various types of data patterns."
      ],
      "metadata": {
        "id": "3lq9qXFFnt6C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(kernel='linear', probability=True)\n",
        "svc.fit(X_train, y_train)\n",
        "y_pred_svc = svc.predict(X_test)\n",
        "accuracy_svc = accuracy_score(y_test, y_pred_svc)\n",
        "print(\"SVC Accuracy:\", accuracy_svc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jXspKcInW4W",
        "outputId": "8f292aa9-4acb-4ce2-a8c6-c297d9a40bf4"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC Accuracy: 0.7941176470588235\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. K-Nearest Neighbors (KNN)"
      ],
      "metadata": {
        "id": "RkxIdFF1n3tn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "How it Works: K-Nearest Neighbors is a non-parametric, instance-based learning algorithm that classifies a new sample based on the majority class among its k-nearest neighbors in the feature space. Distances are calculated (often using Euclidean distance), and the algorithm assigns the label of the most common class among the neighbors.\n",
        "\n",
        "Why to Choose It: KNN is straightforward and doesn’t require assumptions about the data distribution. It’s effective for smaller datasets with distinct clusters and can adapt to data without complex transformations. However, KNN can be computationally expensive for large datasets, so it's usually a candidate for small to moderate data sizes."
      ],
      "metadata": {
        "id": "3qt04Bn_n7dP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred_knn = knn.predict(X_test)\n",
        "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
        "print(\"KNN Accuracy:\", accuracy_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdHe-P-BnxwP",
        "outputId": "6609b73a-e543-4e3a-ae08-3be1b100c932"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Accuracy: 0.7235294117647059\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion for Model Selection and Initial Training"
      ],
      "metadata": {
        "id": "9oo-Y3VKoZlm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the initial results with a 25% test size, we observed the following accuracies:\n",
        "\n",
        "**1. Logistic Regression: 80.0%**\n",
        "\n",
        "**2. Support Vector Classifier (SVC): 79.4%**\n",
        "\n",
        "**3. Random Forest Classifier: 75.3%**\n",
        "\n",
        "**4. K-Nearest Neighbors (KNN): 72.4%**\n",
        "\n",
        "From these results, **Logistic Regression** achieved the highest accuracy, closely followed by **SVC**. Both of these models demonstrated solid performance, indicating that linear or near-linear separability might exist within the data, making these methods effective choices. **Random Forest** and **KNN** showed slightly lower accuracy, but their unique strengths might still provide valuable insights after hyperparameter tuning.\n",
        "\n",
        "Given these results, we’ll proceed with further evaluation of **Logistic Regression and SVC** , as they show the most promising performance. Additionally, we will consider tuning the parameters of **Random Forest** to see if its performance can be improved."
      ],
      "metadata": {
        "id": "lL8_aw2zob99"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "gWwhZ9sxqtff"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning: Overview"
      ],
      "metadata": {
        "id": "6_Q5h_DEpa_5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this next step, we will focus on Hyperparameter Tuning to optimize the performance of our selected models. Hyperparameters are configuration settings of a model that are not learned from the data during training; instead, they are set before the training process begins and directly impact the model's behavior and performance."
      ],
      "metadata": {
        "id": "ttDmWCT_pc42"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Why Hyperparameter Tuning?**\n",
        "Each machine learning model has specific hyperparameters that influence its learning process and the quality of predictions. For example:\n",
        "\n",
        "Logistic Regression has a regularization parameter (C) that controls the trade-off between maximizing accuracy and reducing overfitting.\n",
        "Support Vector Classifier (SVC) has hyperparameters like the kernel type and the C parameter, affecting decision boundaries and margin.\n",
        "Random Forest has parameters like the number of trees (n_estimators) and the depth of each tree (max_depth), which impact model accuracy and robustness.\n",
        "Tuning these parameters allows us to find the best configuration for each model, potentially improving accuracy and other evaluation metrics by avoiding underfitting or overfitting.\n",
        "\n"
      ],
      "metadata": {
        "id": "uh4VEHHqpe-Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **How We Perform Hyperparameter Tuning**\n",
        "We’ll use GridSearchCV, a method that systematically tests combinations of hyperparameters using cross-validation. This approach evaluates multiple configurations to find the optimal settings. Each model has a predefined grid of parameters, which we’ll define based on our knowledge of that model. After tuning, we’ll select the model with the best performance.\n",
        "\n",
        "### **Key Steps**\n",
        "Define Parameter Grid: Specify the range of hyperparameters to test for each model.\n",
        "Run GridSearchCV: Use cross-validation to test each hyperparameter combination.\n",
        "Select the Best Model: Choose the model configuration that yields the highest cross-validated score."
      ],
      "metadata": {
        "id": "4ALDWzmdpp3y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Expected Outcome**\n",
        "Hyperparameter tuning aims to refine our models and enhance their ability to generalize to new data. We’ll then proceed to evaluate the tuned models with additional metrics to confirm their performance on the test set.\n",
        "\n",
        "This process helps us build a robust predictive model, making it more reliable for predicting diabetes in this healthcare dataset."
      ],
      "metadata": {
        "id": "C4UUaxaNpyy8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Logistic Regression\n",
        "param_grid_log = {\n",
        "    'C': [0.1, 1, 10, 100],\n",
        "    'solver': ['liblinear', 'lbfgs']\n",
        "}\n",
        "grid_log = GridSearchCV(LogisticRegression(), param_grid_log, cv=5)\n",
        "grid_log.fit(X_train, y_train)\n",
        "print(\"Best parameters for Logistic Regression:\", grid_log.best_params_)\n",
        "print(\"Best cross-validation score for Logistic Regression:\", grid_log.best_score_)\n",
        "\n",
        "# Support Vector Classifier (SVC)\n",
        "param_grid_svc = {\n",
        "    'C': [0.1, 1, 10, 100],\n",
        "    'kernel': ['linear', 'rbf', 'poly'],\n",
        "    'gamma': ['scale', 'auto']\n",
        "}\n",
        "grid_svc = GridSearchCV(SVC(), param_grid_svc, cv=5)\n",
        "grid_svc.fit(X_train, y_train)\n",
        "print(\"Best parameters for SVC:\", grid_svc.best_params_)\n",
        "print(\"Best cross-validation score for SVC:\", grid_svc.best_score_)\n",
        "\n",
        "# Random Forest Classifier\n",
        "param_grid_rf = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "grid_rf = GridSearchCV(RandomForestClassifier(), param_grid_rf, cv=5)\n",
        "grid_rf.fit(X_train, y_train)\n",
        "print(\"Best parameters for Random Forest:\", grid_rf.best_params_)\n",
        "print(\"Best cross-validation score for Random Forest:\", grid_rf.best_score_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzF1QXXtqCNm",
        "outputId": "2fff5d61-8dbb-4b90-f4cc-1cb52975019b"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters for Logistic Regression: {'C': 10, 'solver': 'liblinear'}\n",
            "Best cross-validation score for Logistic Regression: 0.7834595224228306\n",
            "Best parameters for SVC: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
            "Best cross-validation score for SVC: 0.7775577557755776\n",
            "Best parameters for Random Forest: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
            "Best cross-validation score for Random Forest: 0.7992816928751699\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------"
      ],
      "metadata": {
        "id": "RcEXRX3DrIBo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Retrain The Model"
      ],
      "metadata": {
        "id": "6j1BKSzPrJUZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression"
      ],
      "metadata": {
        "id": "XFIEWrRDrMG9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrain Logistic Regression with the best parameters\n",
        "best_log_model = LogisticRegression(C=10, solver='liblinear')\n",
        "best_log_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "ArhMED3zrOvL",
        "outputId": "ee80704e-0899-46d1-9d9a-e772cc092fd4"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10, solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=10, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=10, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Support Vector Classifier"
      ],
      "metadata": {
        "id": "MmETy1N5rRos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrain SVC with the best parameters\n",
        "best_svc_model = SVC(C=1, gamma='scale', kernel='linear')\n",
        "best_svc_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "IpNtA0aVrVFM",
        "outputId": "f9567ed4-53b4-4d47-b449-3aadfeee19b7"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest Classifier"
      ],
      "metadata": {
        "id": "9U8xxnj7rc4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrain Random Forest with the best parameters\n",
        "best_rf_model = RandomForestClassifier(max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=50)\n",
        "best_rf_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Ew1K0voxqKKQ",
        "outputId": "780b3d07-e1db-40ad-d8f4-bc55e04db8bd"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(min_samples_split=10, n_estimators=50)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_split=10, n_estimators=50)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(min_samples_split=10, n_estimators=50)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "EHsOIpROrmfY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the Models on the Test Set:"
      ],
      "metadata": {
        "id": "ndPEptpArn90"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Logistic Regression**"
      ],
      "metadata": {
        "id": "OGtUXcaprriF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score\n",
        "\n",
        "# Predict using the best Logistic Regression model\n",
        "y_pred_log = best_log_model.predict(X_test)\n",
        "\n",
        "# Accuracy\n",
        "log_accuracy = accuracy_score(y_test, y_pred_log)\n",
        "print(\"Logistic Regression Accuracy:\", log_accuracy)\n",
        "\n",
        "# Classification Report\n",
        "print(\"Classification Report for Logistic Regression:\\n\", classification_report(y_test, y_pred_log))\n",
        "\n",
        "# Confusion Matrix\n",
        "print(\"Confusion Matrix for Logistic Regression:\\n\", confusion_matrix(y_test, y_pred_log))\n",
        "\n",
        "# ROC-AUC Score\n",
        "log_roc_auc = roc_auc_score(y_test, best_log_model.predict_proba(X_test)[:, 1])\n",
        "print(\"ROC-AUC Score for Logistic Regression:\", log_roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUsASjCurh-K",
        "outputId": "4348fa52-edcb-4324-a193-87f7454c7cc2"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 0.8\n",
            "Classification Report for Logistic Regression:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.91      0.86       113\n",
            "           1       0.77      0.58      0.66        57\n",
            "\n",
            "    accuracy                           0.80       170\n",
            "   macro avg       0.79      0.75      0.76       170\n",
            "weighted avg       0.80      0.80      0.79       170\n",
            "\n",
            "Confusion Matrix for Logistic Regression:\n",
            " [[103  10]\n",
            " [ 24  33]]\n",
            "ROC-AUC Score for Logistic Regression: 0.8402421984163949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Support Vector Classifier**"
      ],
      "metadata": {
        "id": "vXplMOzzryKq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrain SVC with the best parameters and enable probability estimates\n",
        "best_svc_model = SVC(C=1, gamma='scale', kernel='linear', probability=True)\n",
        "best_svc_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict using the best SVC model\n",
        "y_pred_svc = best_svc_model.predict(X_test)\n",
        "\n",
        "# Accuracy\n",
        "svc_accuracy = accuracy_score(y_test, y_pred_svc)\n",
        "print(\"SVC Accuracy:\", svc_accuracy)\n",
        "\n",
        "# Classification Report\n",
        "print(\"Classification Report for SVC:\\n\", classification_report(y_test, y_pred_svc))\n",
        "\n",
        "# Confusion Matrix\n",
        "print(\"Confusion Matrix for SVC:\\n\", confusion_matrix(y_test, y_pred_svc))\n",
        "\n",
        "# ROC-AUC Score\n",
        "svc_roc_auc = roc_auc_score(y_test, best_svc_model.predict_proba(X_test)[:, 1])\n",
        "print(\"ROC-AUC Score for SVC:\", svc_roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xo5hf0GcrvXC",
        "outputId": "ced62f4c-2408-4df0-84db-c11d14ae0264"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC Accuracy: 0.7941176470588235\n",
            "Classification Report for SVC:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.92      0.86       113\n",
            "           1       0.78      0.54      0.64        57\n",
            "\n",
            "    accuracy                           0.79       170\n",
            "   macro avg       0.79      0.73      0.75       170\n",
            "weighted avg       0.79      0.79      0.78       170\n",
            "\n",
            "Confusion Matrix for SVC:\n",
            " [[104   9]\n",
            " [ 26  31]]\n",
            "ROC-AUC Score for SVC: 0.8413289861822698\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Random Forest**"
      ],
      "metadata": {
        "id": "QVBGDPQSsf4Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict using the best Random Forest model\n",
        "y_pred_rf = best_rf_model.predict(X_test)\n",
        "\n",
        "# Accuracy\n",
        "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
        "print(\"Random Forest Accuracy:\", rf_accuracy)\n",
        "\n",
        "# Classification Report\n",
        "print(\"Classification Report for Random Forest:\\n\", classification_report(y_test, y_pred_rf))\n",
        "\n",
        "# Confusion Matrix\n",
        "print(\"Confusion Matrix for Random Forest:\\n\", confusion_matrix(y_test, y_pred_rf))\n",
        "\n",
        "# ROC-AUC Score\n",
        "rf_roc_auc = roc_auc_score(y_test, best_rf_model.predict_proba(X_test)[:, 1])\n",
        "print(\"ROC-AUC Score for Random Forest:\", rf_roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9MQKtaXsin2",
        "outputId": "e9a88478-e8e9-44ee-a91e-a933c5b55354"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.7294117647058823\n",
            "Classification Report for Random Forest:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.83      0.80       113\n",
            "           1       0.61      0.53      0.57        57\n",
            "\n",
            "    accuracy                           0.73       170\n",
            "   macro avg       0.69      0.68      0.68       170\n",
            "weighted avg       0.72      0.73      0.72       170\n",
            "\n",
            "Confusion Matrix for Random Forest:\n",
            " [[94 19]\n",
            " [27 30]]\n",
            "ROC-AUC Score for Random Forest: 0.7815556590591524\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "1. Model Comparison:\n",
        "\n",
        "Let’s break down the evaluation results for each model based on key metrics:\n",
        "\n",
        "Logistic Regression:\n",
        "Accuracy: 0.80\n",
        "Precision for Class 0 (No Diabetes): 0.81\n",
        "Recall for Class 0: 0.91\n",
        "Precision for Class 1 (Diabetes): 0.77\n",
        "Recall for Class 1: 0.58\n",
        "F1-score for Class 1: 0.66\n",
        "ROC-AUC: 0.84\n",
        "\n",
        "SVC:\n",
        "Accuracy: 0.79\n",
        "Precision for Class 0: 0.80\n",
        "Recall for Class 0: 0.92\n",
        "Precision for Class 1: 0.78\n",
        "Recall for Class 1: 0.54\n",
        "F1-score for Class 1: 0.64\n",
        "ROC-AUC: 0.84\n",
        "\n",
        "Random Forest:\n",
        "Accuracy: 0.73\n",
        "Precision for Class 0: 0.78\n",
        "Recall for Class 0: 0.83\n",
        "Precision for Class 1: 0.61\n",
        "Recall for Class 1: 0.53\n",
        "F1-score for Class 1: 0.57\n",
        "ROC-AUC: 0.78\n",
        "\n",
        "---\n",
        "\n",
        "2. Key Metrics Consideration:\n",
        "\n",
        "Accuracy: All models have decent accuracy, but Logistic Regression (0.80) and SVC (0.79) are clearly outperforming Random Forest (0.73).\n",
        "\n",
        "Precision and Recall for Class 0 (No Diabetes): SVC has the highest recall (0.92) for Class 0, followed by Logistic Regression (0.91). Random Forest has the\n",
        "lowest recall for Class 0 (0.83).\n",
        "\n",
        "Precision and Recall for Class 1 (Diabetes): Logistic Regression has the highest precision (0.77), while SVC has the highest precision for Class 1 (0.78). However, Logistic Regression has better recall for Class 1 (0.58 vs. 0.54 in SVC).\n",
        "\n",
        "ROC-AUC: Both Logistic Regression and SVC have similar ROC-AUC scores (0.84), which is a good indication that both models perform well at distinguishing between classes. Random Forest has a lower ROC-AUC (0.78), suggesting weaker discriminatory ability.\n",
        "\n",
        "---\n",
        "\n",
        "3. Model Selection:\n",
        "Based on the metrics, we can conclude that:\n",
        "\n",
        "Logistic Regression stands out due to:\n",
        "\n",
        "Higher recall for Class 1 (important for detecting diabetes).\n",
        "High precision for Class 1 as well.\n",
        "Comparable ROC-AUC to SVC.\n",
        "SVC has slightly better recall for Class 0 but performs worse than Logistic Regression on Class 1 metrics.\n",
        "\n",
        "Random Forest shows the weakest performance across the board, especially for Class 1 (both precision and recall). Its accuracy is also the lowest.\n",
        "\n",
        "Thus, Logistic Regression is the most balanced model with the best overall performance for this dataset."
      ],
      "metadata": {
        "id": "1LI1vv2gtg6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrain the final selected model on the entire dataset\n",
        "final_model = LogisticRegression(C=10, solver='liblinear')\n",
        "final_model.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "2dQV-Cl4r123",
        "outputId": "65da691d-63ee-4124-f058-f592100f7073"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10, solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=10, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=10, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting on the entire dataset (or test set if you have a separate one)\n",
        "y_pred = final_model.predict(X)"
      ],
      "metadata": {
        "id": "HGlz4_YfuOpX"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy = accuracy_score(y, y_pred)\n",
        "print(f\"Accuracy of the final model: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRnYYmyYurY8",
        "outputId": "32430e80-d31a-4ecb-fe05-df378277aa6b"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the final model: 0.7979351032448377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQPiKi_lutMw",
        "outputId": "a915e2f2-4f35-4c0d-b503-5700856ed83a"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[410  42]\n",
            " [ 95 131]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "report = classification_report(y, y_pred)\n",
        "print(\"Classification Report:\")\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzhlEWbRuu2u",
        "outputId": "8c59627d-4eff-4603-cbd2-7d2302592119"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.91      0.86       452\n",
            "           1       0.76      0.58      0.66       226\n",
            "\n",
            "    accuracy                           0.80       678\n",
            "   macro avg       0.78      0.74      0.76       678\n",
            "weighted avg       0.79      0.80      0.79       678\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "roc_auc = roc_auc_score(y, final_model.predict_proba(X)[:, 1])\n",
        "print(f\"ROC-AUC Score: {roc_auc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFIZOv8auwe0",
        "outputId": "a23a2c8f-b57a-4eb3-f45c-81f98ef3ca8e"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC-AUC Score: 0.8508497141514606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Summary of Completed Steps:**\n",
        "1. Data Preprocessing: We handled missing values, detected and treated outliers, and thoroughly explored the data to ensure it was clean and ready for modeling.\n",
        "\n",
        "2. Model Training: We trained multiple models, including Logistic Regression, SVC, and Random Forest, using the preprocessed data to compare their performances.\n",
        "\n",
        "3. Hyperparameter Tuning: We fine-tuned the hyperparameters of each model using GridSearchCV to improve their performance.\n",
        "\n",
        "4. Model Evaluation: We evaluated each model on the test data using key metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC score to assess their strengths and weaknesses.\n",
        "\n",
        "5. Model Comparison: After evaluating the models, we compared their results and found that Logistic Regression outperformed the other models in terms of precision, recall, and ROC-AUC for Class 1 (Diabetes). Since detecting diabetes is the most important task, Logistic Regression was the best choice.\n"
      ],
      "metadata": {
        "id": "VgA0Lei1vKkK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Conclusion:**\n",
        "The best-performing model for our use case is **Logistic Regression**. It provides the most balanced performance across key metrics, especially in terms of detecting diabetes (Class 1)."
      ],
      "metadata": {
        "id": "hYNzdj-jvj2W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Next Steps:**\n",
        "\n",
        "1. Retrain the Selected Model: We will retrain Logistic Regression using the entire dataset (both the training and test data).\n",
        "\n",
        "2. Model Saving: Once retrained, we will save the final model so we can use it for future predictions without the need for retraining.\n",
        "\n",
        "3. Deployment (Optional): If we wish to deploy the model, we can integrate it into a web application or use it for real-time predictions.\n",
        "\n",
        "4. Model Interpretation (Optional): If needed, we can explore how the model is making its decisions. Tools like SHAP or LIME can help with model interpretability to better understand feature importance and decision-making."
      ],
      "metadata": {
        "id": "yziRH-9GvnNE"
      }
    }
  ]
}