ML_Algorithm(diabetes)_(Shoaib_MTalib_Aashir).ipynb

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        "<a href=\"https://colab.research.google.com/github/mtalibfarooq/Machine_Learning_Diabetes_Dataset/blob/main/ML_Algorithm(diabetes)_(Shoaib_MTalib_Aashir).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 49,
      "id": "c267a48d",
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        "id": "c267a48d"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "af5ff3e9",
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      "source": [
        "def extract(src):\n",
        "    df = pd.read_csv(src)\n",
        "    print(f\"Rows: {df.shape[0]},\\tColumns: {df.shape[1]}\")\n",
        "\n",
        "    df = df.rename(columns=lambda x: x.replace(\" \", \"\"))\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
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      "id": "1a7b6393",
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        "id": "1a7b6393",
        "outputId": "c3cec6a1-48a6-4f3e-f1e2-e798f441d52c"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 768,\tColumns: 9\n"
          ]
        },
        {
          "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></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
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              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
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          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "data = extract(\"/content/diabetes.csv\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "0eeb84c8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eeb84c8",
        "outputId": "966d51eb-71c3-4337-fa19-3a6592b3649f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "data['Outcome'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32fd1080",
      "metadata": {
        "id": "32fd1080"
      },
      "source": [
        "# Target/Label/Outcome Visualization of No of Diabetes patients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "ace91601",
      "metadata": {
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          "height": 411
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        "id": "ace91601",
        "outputId": "19970eff-7689-42af-ef13-fc97b2f0e739"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 4))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.countplot(x=\"Outcome\", data=data, palette=\"Set2\")\n",
        "plt.title(\"Number of Target\")\n",
        "plt.xlabel(\"0 = No Disease | 1 = Disease\")\n",
        "plt.ylabel(\"Count\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "target_counts = data['Outcome'].value_counts()\n",
        "plt.pie(target_counts.values, labels=[\"Disease\", \"No Disease\"], shadow=True, autopct=\"%1.1f%%\", colors=[\"#B7C3F3\", \"#8EB897\"])\n",
        "plt.legend(title=\"Target:\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "2f1414cb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2f1414cb",
        "outputId": "31a0190f-5b6d-4e17-f4b5-c0beebe1d95d"
      },
      "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"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "e16c997a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "e16c997a",
        "outputId": "8bc77364-4202-48b9-8977-1600375b0364"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get the correlation matrix as a Pandas DataFrame\n",
        "correlation_matrix = data.corr()\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(10, 8))\n",
        "\n",
        "# Create the heatmap\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "37dd2c23",
      "metadata": {
        "id": "37dd2c23"
      },
      "source": [
        "Number of Instances: 768\n",
        "Number of Attributes: 8 plus target/class\n",
        "For Each Attribute: (all numeric-valued)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1b2f488e",
      "metadata": {
        "id": "1b2f488e"
      },
      "source": [
        "Pregnancies: Number of times pregnant\n",
        "Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
        "BloodPressure: Diastolic blood pressure (mm Hg)\n",
        "SkinThickness: Triceps skin fold thickness (mm)\n",
        "Insulin: 2-Hour serum insulin (mu U/ml)\n",
        "BMI: Body mass index (weight in kg/(height in m)^2)\n",
        "DiabetesPedigreeFunction: Diabetes pedigree function\n",
        "Age: Age (years)\n",
        "Outcome: Class variable (0 or 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Selection\n",
        "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
        "\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JEUkxUqt4NHH",
        "outputId": "995bd592-9430-4c45-d8cf-fa9dd8730c9d"
      },
      "id": "JEUkxUqt4NHH",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
              "0            6      148        0  33.6                     0.627   50\n",
              "1            1       85        0  26.6                     0.351   31\n",
              "2            8      183        0  23.3                     0.672   32\n",
              "3            1       89       94  28.1                     0.167   21\n",
              "4            0      137      168  43.1                     2.288   33"
            ],
            "text/html": [
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              "      <th>4</th>\n",
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              "      <td>43.1</td>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e1f3410a-cd3b-473d-a854-c6d3e8b02731');\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",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0b8a1525-1e28-4ce5-83e4-46b87168f10d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "24363fb7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24363fb7",
        "outputId": "bf981afc-cf11-42fd-9952-de6f3d4a986d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "y=data['Outcome']\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "cdb7be49",
      "metadata": {
        "id": "cdb7be49"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "b968e577",
      "metadata": {
        "id": "b968e577"
      },
      "outputs": [],
      "source": [
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "ce4a75f2",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ce4a75f2",
        "outputId": "a17ec1cb-6af8-4e28-d1e5-ca02c67130bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
              "446            1      100       70  25.3                     0.658   28\n",
              "260            3      191      130  30.9                     0.299   34\n",
              "570            3       78        0  32.5                     0.270   39\n",
              "590           11      111        0  46.8                     0.925   45\n",
              "30             5      109        0  36.0                     0.546   60"
            ],
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              "\n",
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              "    <tr>\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4803d8f7-6468-47ba-9373-24d859437d5e 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-4803d8f7-6468-47ba-9373-24d859437d5e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-db069d04-cfc7-4a46-9765-cd38ea7d8bbe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "xtrain.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "89862d72",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89862d72",
        "outputId": "7e480ead-3d11-4d45-f98c-a2925affe84d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "446    0\n",
              "260    0\n",
              "570    0\n",
              "590    1\n",
              "30     0\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "ytrain.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "e09b3518",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "e09b3518",
        "outputId": "3d224034-26c0-4006-9558-e9e76f2cb9bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "74351b00",
      "metadata": {
        "id": "74351b00"
      },
      "outputs": [],
      "source": [
        "predictions = model.predict(xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "a7f47590",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7f47590",
        "outputId": "8fd6f9fc-96d0-4dd6-9cca-a03903482418"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
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            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "2ebb0ed8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ebb0ed8",
        "outputId": "880b6627-07a8-4fb3-b998-fb13ecb5a10d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "143    1\n",
              "14     1\n",
              "504    0\n",
              "557    0\n",
              "736    0\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "ytest.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "ce1fa137",
      "metadata": {
        "id": "ce1fa137"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "8f35c95c",
      "metadata": {
        "id": "8f35c95c"
      },
      "outputs": [],
      "source": [
        "acc = accuracy_score(predictions,ytest)\n",
        "cm = confusion_matrix(predictions,ytest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "ce22536c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ce22536c",
        "outputId": "d9276ebc-26eb-44bd-c855-a95a408f2752"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7727272727272727"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "7f224373",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7f224373",
        "outputId": "74ec30ac-e4e4-4b6c-88d1-0d0b4f5352df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[91, 25],\n",
              "       [10, 28]])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "3209ef1f",
      "metadata": {
        "id": "3209ef1f"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "ca421dad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ca421dad",
        "outputId": "9ad8efbe-f76b-4d5d-9ea6-ca4540300f6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.90      0.84       101\n",
            "           1       0.74      0.53      0.62        53\n",
            "\n",
            "    accuracy                           0.77       154\n",
            "   macro avg       0.76      0.71      0.73       154\n",
            "weighted avg       0.77      0.77      0.76       154\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(ytest,predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e569da9",
      "metadata": {
        "id": "4e569da9"
      },
      "source": [
        "# Decision Tree classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "6ec11914",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6ec11914",
        "outputId": "bcc7a117-9a7e-4997-9993-440d2fd2a093"
      },
      "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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55e74d8a-c963-4efc-a7b9-78729061989a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Selection\n",
        "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
        "\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9Mo89a5b5mfx",
        "outputId": "aff2b26a-3845-4f15-d64d-e9d764a71304"
      },
      "id": "9Mo89a5b5mfx",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
              "0            6      148        0  33.6                     0.627   50\n",
              "1            1       85        0  26.6                     0.351   31\n",
              "2            8      183        0  23.3                     0.672   32\n",
              "3            1       89       94  28.1                     0.167   21\n",
              "4            0      137      168  43.1                     2.288   33"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e0eef7e-0468-4c7a-bd7b-32588aab9456\" 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>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</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-6e0eef7e-0468-4c7a-bd7b-32588aab9456')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e0eef7e-0468-4c7a-bd7b-32588aab9456 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-6e0eef7e-0468-4c7a-bd7b-32588aab9456');\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-b2f6afed-9bd1-46a1-8e95-a7c3f3dac89a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b2f6afed-9bd1-46a1-8e95-a7c3f3dac89a')\"\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",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b2f6afed-9bd1-46a1-8e95-a7c3f3dac89a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "9d87a616",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d87a616",
        "outputId": "74c303f9-116b-4472-d99d-6275fd21d221"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "y=data['Outcome']\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "bd99f1ab",
      "metadata": {
        "id": "bd99f1ab"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "b8759841",
      "metadata": {
        "id": "b8759841"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "f0901b18",
      "metadata": {
        "id": "f0901b18"
      },
      "outputs": [],
      "source": [
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6eb945d5",
      "metadata": {
        "id": "6eb945d5"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "273f3ee9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "273f3ee9",
        "outputId": "2c57d1a5-1667-4475-def5-794a39651934"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "model = DecisionTreeClassifier()\n",
        "model.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "389f79a4",
      "metadata": {
        "id": "389f79a4"
      },
      "outputs": [],
      "source": [
        "pre = model.predict(xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "49f2caec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49f2caec",
        "outputId": "b077a220-e9be-4380-e9b3-98b59b9b34c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.66      0.71       101\n",
            "           1       0.49      0.62      0.55        53\n",
            "\n",
            "    accuracy                           0.65       154\n",
            "   macro avg       0.63      0.64      0.63       154\n",
            "weighted avg       0.67      0.65      0.66       154\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[67, 20],\n",
              "       [34, 33]])"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "ass=accuracy_score(pre,ytest)\n",
        "ass\n",
        "cm = confusion_matrix(pre,ytest)\n",
        "print(classification_report(ytest,pre))\n",
        "ass\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02f7c37d",
      "metadata": {
        "id": "02f7c37d"
      },
      "source": [
        "# Random Forest classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "9a1bc4c4",
      "metadata": {
        "id": "9a1bc4c4"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "a3f6cb27",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a3f6cb27",
        "outputId": "faed2417-480d-4b37-f56b-9e40e4a4c4a7"
      },
      "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac539ee1-aef5-4be7-ac94-2ec1b637ec5f\" class=\"colab-df-container\">\n",
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              "</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",
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              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
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              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</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>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</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-ac539ee1-aef5-4be7-ac94-2ec1b637ec5f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ac539ee1-aef5-4be7-ac94-2ec1b637ec5f 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-ac539ee1-aef5-4be7-ac94-2ec1b637ec5f');\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-4bc3a0df-2948-4c7e-97c1-68a717935366\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bc3a0df-2948-4c7e-97c1-68a717935366')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bc3a0df-2948-4c7e-97c1-68a717935366 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "1dfd6b5c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1dfd6b5c",
        "outputId": "1a5e47b6-4cec-4b95-9ce7-460fe6f37b3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
              "0            6      148        0  33.6                     0.627   50\n",
              "1            1       85        0  26.6                     0.351   31\n",
              "2            8      183        0  23.3                     0.672   32\n",
              "3            1       89       94  28.1                     0.167   21\n",
              "4            0      137      168  43.1                     2.288   33"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47c858af-5ea4-41a8-bc8e-0ac59d8fb593\" 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>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</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-47c858af-5ea4-41a8-bc8e-0ac59d8fb593')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-47c858af-5ea4-41a8-bc8e-0ac59d8fb593 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-47c858af-5ea4-41a8-bc8e-0ac59d8fb593');\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-307eef1c-4424-4844-b538-cd60d2a6b176\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-307eef1c-4424-4844-b538-cd60d2a6b176')\"\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",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-307eef1c-4424-4844-b538-cd60d2a6b176 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "#Feature Selection\n",
        "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
        "\n",
        "x.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "9058d90e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9058d90e",
        "outputId": "fbdd5ce7-28a3-4bab-92f8-f08b84001994"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "y=data['Outcome']\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "ca1e3c1d",
      "metadata": {
        "id": "ca1e3c1d"
      },
      "outputs": [],
      "source": [
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "754f8c68",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "754f8c68",
        "outputId": "a486864f-f819-4d30-f29b-522673c0f42c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "model = RandomForestClassifier()\n",
        "model.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "b7a87116",
      "metadata": {
        "id": "b7a87116"
      },
      "outputs": [],
      "source": [
        "pr = model.predict(xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "8ddd3fa2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ddd3fa2",
        "outputId": "bc6cc89e-5da8-42aa-88a1-dd76b3039b73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.86      0.81        94\n",
            "           1       0.73      0.58      0.65        60\n",
            "\n",
            "    accuracy                           0.75       154\n",
            "   macro avg       0.75      0.72      0.73       154\n",
            "weighted avg       0.75      0.75      0.75       154\n",
            "\n"
          ]
        }
      ],
      "source": [
        "ass = accuracy_score(ytest,pr)\n",
        "ass\n",
        "cm = confusion_matrix(pr,ytest)\n",
        "cm\n",
        "print(classification_report(ytest,pr))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "532686fd",
      "metadata": {
        "id": "532686fd"
      },
      "source": [
        "# KNN Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "19ecff71",
      "metadata": {
        "id": "19ecff71"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,f1_score\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "821b8b12",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "821b8b12",
        "outputId": "68505ba4-3c07-4b80-eddc-5ac1b0fa9245"
      },
      "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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              "\n",
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              "        document.querySelector('#df-88b759d0-66cf-4643-8be0-b9e8f214af3a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-88b759d0-66cf-4643-8be0-b9e8f214af3a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
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              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "      let quickchartButtonEl =\n",
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            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "a0590b0f",
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "a0590b0f",
        "outputId": "83bcd548-4eb1-470a-f467-ec9543d5a571"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
              "0            6      148        0  33.6                     0.627   50\n",
              "1            1       85        0  26.6                     0.351   31\n",
              "2            8      183        0  23.3                     0.672   32\n",
              "3            1       89       94  28.1                     0.167   21\n",
              "4            0      137      168  43.1                     2.288   33"
            ],
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              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</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-c0534d92-f77c-44bd-b44e-1e2c833bab57')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c0534d92-f77c-44bd-b44e-1e2c833bab57 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-c0534d92-f77c-44bd-b44e-1e2c833bab57');\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-5d7dcb7d-dda9-4290-adc4-738abeaee0bb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5d7dcb7d-dda9-4290-adc4-738abeaee0bb')\"\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",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5d7dcb7d-dda9-4290-adc4-738abeaee0bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "#Feature Selection\n",
        "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
        "\n",
        "x.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "802b9d25",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "802b9d25",
        "outputId": "a673e624-bb6e-4ed2-b2a4-a2123a51f15f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "y=data['Outcome']\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "18f0ed6e",
      "metadata": {
        "id": "18f0ed6e"
      },
      "outputs": [],
      "source": [
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Calculate accuracy for different values of n_neighbors\n",
        "neighbors = np.arange(1, 21)  # Test n_neighbors from 1 to 20\n",
        "train_accuracy = []\n",
        "test_accuracy = []\n",
        "\n",
        "for n in neighbors:\n",
        "    mdl = KNeighborsClassifier(n_neighbors=n)\n",
        "    mdl.fit(xtrain, ytrain)\n",
        "    train_accuracy.append(mdl.score(xtrain, ytrain))\n",
        "    test_accuracy.append(mdl.score(xtest, ytest))\n",
        "\n",
        "# Plotting the elbow curve\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(neighbors, train_accuracy, label=\"Train Accuracy\")\n",
        "plt.plot(neighbors, test_accuracy, label=\"Test Accuracy\")\n",
        "plt.xlabel(\"Number of Neighbors\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Elbow Method for Optimal n_neighbors\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "QvDbFG67AJfZ",
        "outputId": "50372e0e-787d-42f6-fe04-c842f636cec5"
      },
      "id": "QvDbFG67AJfZ",
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "id": "b31be336",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "b31be336",
        "outputId": "675d0722-ee85-4a28-9bad-05b2cc33f69d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;, n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;, n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ],
      "source": [
        "mdl = KNeighborsClassifier(n_neighbors=3,p=2,metric='euclidean')\n",
        "mdl.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "id": "33c7e7e8",
      "metadata": {
        "id": "33c7e7e8"
      },
      "outputs": [],
      "source": [
        "prediction = mdl.predict(xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "id": "7da3e6c4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7da3e6c4",
        "outputId": "6a59756f-45a9-4572-ecf9-fafeb7c29b7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6558441558441559\n",
            "[[78 25]\n",
            " [28 23]]\n",
            "0.46464646464646464\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.76      0.75       103\n",
            "           1       0.48      0.45      0.46        51\n",
            "\n",
            "    accuracy                           0.66       154\n",
            "   macro avg       0.61      0.60      0.61       154\n",
            "weighted avg       0.65      0.66      0.65       154\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(accuracy_score(ytest,prediction))\n",
        "print(confusion_matrix(ytest,prediction))\n",
        "print(f1_score(ytest,prediction))\n",
        "print(classification_report(ytest,prediction))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}