voting_algorithm_classifier_100.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "0WHsy2ZbYjXf"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1oHhqdjYvAK",
        "outputId": "7ebfe2ac-6ba8-4882-bfcb-eb85de05c3f8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UxirizVGYjXi"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "#Evaluvating\n",
        "from sklearn.metrics import classification_report,confusion_matrix,roc_curve,RocCurveDisplay,precision_score,recall_score,f1_score,ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import train_test_split,RandomizedSearchCV,GridSearchCV,cross_val_score\n",
        "from sklearn.ensemble import VotingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ytLr2Ly0YjXi"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "lL_349VjYjXj"
      },
      "outputs": [],
      "source": [
        "features=pd.read_csv('/content/drive/MyDrive/feature_100_true.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
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        "id": "Cug3xJiFYjXj",
        "outputId": "1cc34c9c-7d62-48a9-c2c3-4a4498ee5b51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "     Pen Pressure  Line Spacing  Slant Angle  Baseline angle  \\\n",
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              "\n",
              "                                                  Link lable        l1  \\\n",
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              "..                                                 ...   ...       ...   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vfWtkAfYjXj",
        "outputId": "aa2faa81-3991-4bde-cd56-dd102e237553"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 206 entries, 0 to 205\n",
            "Data columns (total 14 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Pen Pressure    206 non-null    float64\n",
            " 1   Line Spacing    206 non-null    float64\n",
            " 2   Slant Angle     206 non-null    int64  \n",
            " 3   Baseline angle  206 non-null    float64\n",
            " 4   Link            206 non-null    object \n",
            " 5   lable           206 non-null    object \n",
            " 6   l1              206 non-null    float64\n",
            " 7   l2              206 non-null    float64\n",
            " 8   l3              206 non-null    float64\n",
            " 9   l4              206 non-null    float64\n",
            " 10  l5              206 non-null    float64\n",
            " 11  l6              206 non-null    float64\n",
            " 12  l7              206 non-null    float64\n",
            " 13  l8              206 non-null    float64\n",
            "dtypes: float64(11), int64(1), object(2)\n",
            "memory usage: 22.7+ KB\n"
          ]
        }
      ],
      "source": [
        "features.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "cTFzCDudYjXk"
      },
      "outputs": [],
      "source": [
        "labels=features['lable']\n",
        "features.drop(['Link','lable'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Sz2DBH9nYjXk",
        "outputId": "71ff0629-d64d-4e49-c97b-686d58fe0e7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pen Pressure  Line Spacing  Slant Angle  Baseline angle        l1  \\\n",
              "0       28.944342   1106.960000            5        0.000000 -1.111134   \n",
              "1       43.559691    617.000000            5        0.000000 -1.082647   \n",
              "2       87.790334   2186.500000            5     1363.664609 -1.183598   \n",
              "3       86.561794    602.000000           -5        0.000000 -1.097709   \n",
              "4       61.942587    701.352041           -5        0.000000 -1.269350   \n",
              "..            ...           ...          ...             ...       ...   \n",
              "201    308.400209    210.000000            5       23.482151 -2.470516   \n",
              "202    549.311920    160.000000            5       97.446474 -2.504734   \n",
              "203     12.188425  32473.760000            5        0.000000 -2.210393   \n",
              "204      0.086254    899.000000            5        0.000000 -2.233008   \n",
              "205    135.671924      0.000000           -5      329.415768 -2.659174   \n",
              "\n",
              "            l2        l3        l4        l5        l6        l7         l8  \n",
              "0     2.815558 -1.963040 -0.453976 -0.714596  5.369467 -1.070979  -1.788626  \n",
              "1     3.269634 -1.901758 -0.508880 -0.687258  5.052329 -1.058053  -1.636201  \n",
              "2     2.349073 -2.086748 -0.382516 -0.695264  6.111055 -1.161153  -2.142544  \n",
              "3     3.170597 -1.899564 -0.520904 -0.703620  5.084200 -1.051114  -1.654392  \n",
              "4     2.046691 -2.122050 -0.381242 -0.751105  6.334366 -1.142994  -2.280844  \n",
              "..         ...       ...       ...       ...       ...       ...        ...  \n",
              "201  11.562148  2.190010 -1.462264 -2.349383 -0.382875 -0.764947  14.053461  \n",
              "202  11.576081  1.995893 -1.411055 -2.212195 -0.285212 -0.695404  13.706995  \n",
              "203   9.209371  0.707892 -0.966253 -1.476118 -0.128085 -0.404649   7.830327  \n",
              "204  10.107451  1.766552 -1.166585 -2.060947 -0.289563 -0.524454  11.425462  \n",
              "205  12.040603  2.397348 -1.543056 -2.552639 -0.426635 -0.817882  14.874842  \n",
              "\n",
              "[206 rows x 12 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pen Pressure</th>\n",
              "      <th>Line Spacing</th>\n",
              "      <th>Slant Angle</th>\n",
              "      <th>Baseline angle</th>\n",
              "      <th>l1</th>\n",
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              "      <td>-1.058053</td>\n",
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              "      <th>2</th>\n",
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              "      <td>-0.695264</td>\n",
              "      <td>6.111055</td>\n",
              "      <td>-1.161153</td>\n",
              "      <td>-2.142544</td>\n",
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              "      <td>-0.703620</td>\n",
              "      <td>5.084200</td>\n",
              "      <td>-1.051114</td>\n",
              "      <td>-1.654392</td>\n",
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              "      <th>4</th>\n",
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              "      <td>701.352041</td>\n",
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              "      <td>2.046691</td>\n",
              "      <td>-2.122050</td>\n",
              "      <td>-0.381242</td>\n",
              "      <td>-0.751105</td>\n",
              "      <td>6.334366</td>\n",
              "      <td>-1.142994</td>\n",
              "      <td>-2.280844</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>-0.764947</td>\n",
              "      <td>14.053461</td>\n",
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              "    <tr>\n",
              "      <th>202</th>\n",
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              "      <td>1.995893</td>\n",
              "      <td>-1.411055</td>\n",
              "      <td>-2.212195</td>\n",
              "      <td>-0.285212</td>\n",
              "      <td>-0.695404</td>\n",
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              "      <td>-0.404649</td>\n",
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              "      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-7f14d424-857a-4ef6-b6bb-8f82f33edd55 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-7f14d424-857a-4ef6-b6bb-8f82f33edd55');\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-d97b5e6e-28d7-4690-90a6-b3dd33713c8c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d97b5e6e-28d7-4690-90a6-b3dd33713c8c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d97b5e6e-28d7-4690-90a6-b3dd33713c8c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c23ef3ec-601b-4679-a0fd-a0210974a6c7\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('features')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c23ef3ec-601b-4679-a0fd-a0210974a6c7 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('features');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "mjnDyfl0YjXk",
        "outputId": "03e4d7f8-3c7d-44c1-c82a-67f908d56c26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0, 'Pen Pressure'), Text(0, 0.5, 'Baseline angle')]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ig,ax=plt.subplots(figsize=(10,10))\n",
        "mp=ax.scatter(features['Pen Pressure'],features['Baseline angle'])\n",
        "#mp=ax.scatter(dk.age[dk.cp==0],dk.trestbps[dk.cp==0])\n",
        "ax.legend\n",
        "ax.set(xlabel=\"Pen Pressure\",ylabel=\"Baseline angle\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "kRUbrp8qYjXl",
        "outputId": "1c3dcab3-e648-495b-d30f-31dff72db9ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0, 'Slant angle'), Text(0, 0.5, 'Baseline angle')]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ig,ax=plt.subplots(figsize=(10,10))\n",
        "mp=ax.scatter(features['Slant Angle'],features['Baseline angle'])\n",
        "\n",
        "ax.legend\n",
        "ax.set(xlabel=\"Slant angle\",ylabel=\"Baseline angle\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "9fCvBTJLYjXl",
        "outputId": "cd2399a3-37d2-4660-f837-1904aea1c2e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Pen Pressure  Line Spacing  Slant Angle  Baseline angle  \\\n",
              "Pen Pressure        1.000000     -0.100115    -0.037254        0.160411   \n",
              "Line Spacing       -0.100115      1.000000    -0.035870       -0.037531   \n",
              "Slant Angle        -0.037254     -0.035870     1.000000       -0.011291   \n",
              "Baseline angle      0.160411     -0.037531    -0.011291        1.000000   \n",
              "l1                 -0.379614      0.022608    -0.113088       -0.255012   \n",
              "l2                  0.377653     -0.005197     0.092449        0.266503   \n",
              "l3                  0.383667     -0.024523     0.097602        0.272545   \n",
              "l4                 -0.369911      0.021722    -0.101527       -0.257733   \n",
              "l5                 -0.378088      0.032742    -0.103165       -0.269727   \n",
              "l6                 -0.341454     -0.016334    -0.099271       -0.258501   \n",
              "l7                  0.153182      0.069362     0.071761        0.156522   \n",
              "l8                  0.392611     -0.019801     0.090370        0.281102   \n",
              "\n",
              "                      l1        l2        l3        l4        l5        l6  \\\n",
              "Pen Pressure   -0.379614  0.377653  0.383667 -0.369911 -0.378088 -0.341454   \n",
              "Line Spacing    0.022608 -0.005197 -0.024523  0.021722  0.032742 -0.016334   \n",
              "Slant Angle    -0.113088  0.092449  0.097602 -0.101527 -0.103165 -0.099271   \n",
              "Baseline angle -0.255012  0.266503  0.272545 -0.257733 -0.269727 -0.258501   \n",
              "l1              1.000000 -0.916508 -0.950642  0.924590  0.978108  0.803957   \n",
              "l2             -0.916508  1.000000  0.987263 -0.992311 -0.955408 -0.963850   \n",
              "l3             -0.950642  0.987263  1.000000 -0.988496 -0.982620 -0.925208   \n",
              "l4              0.924590 -0.992311 -0.988496  1.000000  0.964952  0.942864   \n",
              "l5              0.978108 -0.955408 -0.982620  0.964952  1.000000  0.863321   \n",
              "l6              0.803957 -0.963850 -0.925208  0.942864  0.863321  1.000000   \n",
              "l7             -0.275674  0.559618  0.463418 -0.498247 -0.345627 -0.748459   \n",
              "l8             -0.954165  0.986866  0.996159 -0.986078 -0.987269 -0.920438   \n",
              "\n",
              "                      l7        l8  \n",
              "Pen Pressure    0.153182  0.392611  \n",
              "Line Spacing    0.069362 -0.019801  \n",
              "Slant Angle     0.071761  0.090370  \n",
              "Baseline angle  0.156522  0.281102  \n",
              "l1             -0.275674 -0.954165  \n",
              "l2              0.559618  0.986866  \n",
              "l3              0.463418  0.996159  \n",
              "l4             -0.498247 -0.986078  \n",
              "l5             -0.345627 -0.987269  \n",
              "l6             -0.748459 -0.920438  \n",
              "l7              1.000000  0.455670  \n",
              "l8              0.455670  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa2d6303-801e-4b77-850b-149e5541def1\" 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>Pen Pressure</th>\n",
              "      <th>Line Spacing</th>\n",
              "      <th>Slant Angle</th>\n",
              "      <th>Baseline angle</th>\n",
              "      <th>l1</th>\n",
              "      <th>l2</th>\n",
              "      <th>l3</th>\n",
              "      <th>l4</th>\n",
              "      <th>l5</th>\n",
              "      <th>l6</th>\n",
              "      <th>l7</th>\n",
              "      <th>l8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pen Pressure</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.100115</td>\n",
              "      <td>-0.037254</td>\n",
              "      <td>0.160411</td>\n",
              "      <td>-0.379614</td>\n",
              "      <td>0.377653</td>\n",
              "      <td>0.383667</td>\n",
              "      <td>-0.369911</td>\n",
              "      <td>-0.378088</td>\n",
              "      <td>-0.341454</td>\n",
              "      <td>0.153182</td>\n",
              "      <td>0.392611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Line Spacing</th>\n",
              "      <td>-0.100115</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.035870</td>\n",
              "      <td>-0.037531</td>\n",
              "      <td>0.022608</td>\n",
              "      <td>-0.005197</td>\n",
              "      <td>-0.024523</td>\n",
              "      <td>0.021722</td>\n",
              "      <td>0.032742</td>\n",
              "      <td>-0.016334</td>\n",
              "      <td>0.069362</td>\n",
              "      <td>-0.019801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Slant Angle</th>\n",
              "      <td>-0.037254</td>\n",
              "      <td>-0.035870</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.011291</td>\n",
              "      <td>-0.113088</td>\n",
              "      <td>0.092449</td>\n",
              "      <td>0.097602</td>\n",
              "      <td>-0.101527</td>\n",
              "      <td>-0.103165</td>\n",
              "      <td>-0.099271</td>\n",
              "      <td>0.071761</td>\n",
              "      <td>0.090370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baseline angle</th>\n",
              "      <td>0.160411</td>\n",
              "      <td>-0.037531</td>\n",
              "      <td>-0.011291</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.255012</td>\n",
              "      <td>0.266503</td>\n",
              "      <td>0.272545</td>\n",
              "      <td>-0.257733</td>\n",
              "      <td>-0.269727</td>\n",
              "      <td>-0.258501</td>\n",
              "      <td>0.156522</td>\n",
              "      <td>0.281102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l1</th>\n",
              "      <td>-0.379614</td>\n",
              "      <td>0.022608</td>\n",
              "      <td>-0.113088</td>\n",
              "      <td>-0.255012</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.916508</td>\n",
              "      <td>-0.950642</td>\n",
              "      <td>0.924590</td>\n",
              "      <td>0.978108</td>\n",
              "      <td>0.803957</td>\n",
              "      <td>-0.275674</td>\n",
              "      <td>-0.954165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l2</th>\n",
              "      <td>0.377653</td>\n",
              "      <td>-0.005197</td>\n",
              "      <td>0.092449</td>\n",
              "      <td>0.266503</td>\n",
              "      <td>-0.916508</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.987263</td>\n",
              "      <td>-0.992311</td>\n",
              "      <td>-0.955408</td>\n",
              "      <td>-0.963850</td>\n",
              "      <td>0.559618</td>\n",
              "      <td>0.986866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l3</th>\n",
              "      <td>0.383667</td>\n",
              "      <td>-0.024523</td>\n",
              "      <td>0.097602</td>\n",
              "      <td>0.272545</td>\n",
              "      <td>-0.950642</td>\n",
              "      <td>0.987263</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.988496</td>\n",
              "      <td>-0.982620</td>\n",
              "      <td>-0.925208</td>\n",
              "      <td>0.463418</td>\n",
              "      <td>0.996159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l4</th>\n",
              "      <td>-0.369911</td>\n",
              "      <td>0.021722</td>\n",
              "      <td>-0.101527</td>\n",
              "      <td>-0.257733</td>\n",
              "      <td>0.924590</td>\n",
              "      <td>-0.992311</td>\n",
              "      <td>-0.988496</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.964952</td>\n",
              "      <td>0.942864</td>\n",
              "      <td>-0.498247</td>\n",
              "      <td>-0.986078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l5</th>\n",
              "      <td>-0.378088</td>\n",
              "      <td>0.032742</td>\n",
              "      <td>-0.103165</td>\n",
              "      <td>-0.269727</td>\n",
              "      <td>0.978108</td>\n",
              "      <td>-0.955408</td>\n",
              "      <td>-0.982620</td>\n",
              "      <td>0.964952</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.863321</td>\n",
              "      <td>-0.345627</td>\n",
              "      <td>-0.987269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l6</th>\n",
              "      <td>-0.341454</td>\n",
              "      <td>-0.016334</td>\n",
              "      <td>-0.099271</td>\n",
              "      <td>-0.258501</td>\n",
              "      <td>0.803957</td>\n",
              "      <td>-0.963850</td>\n",
              "      <td>-0.925208</td>\n",
              "      <td>0.942864</td>\n",
              "      <td>0.863321</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.748459</td>\n",
              "      <td>-0.920438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l7</th>\n",
              "      <td>0.153182</td>\n",
              "      <td>0.069362</td>\n",
              "      <td>0.071761</td>\n",
              "      <td>0.156522</td>\n",
              "      <td>-0.275674</td>\n",
              "      <td>0.559618</td>\n",
              "      <td>0.463418</td>\n",
              "      <td>-0.498247</td>\n",
              "      <td>-0.345627</td>\n",
              "      <td>-0.748459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.455670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l8</th>\n",
              "      <td>0.392611</td>\n",
              "      <td>-0.019801</td>\n",
              "      <td>0.090370</td>\n",
              "      <td>0.281102</td>\n",
              "      <td>-0.954165</td>\n",
              "      <td>0.986866</td>\n",
              "      <td>0.996159</td>\n",
              "      <td>-0.986078</td>\n",
              "      <td>-0.987269</td>\n",
              "      <td>-0.920438</td>\n",
              "      <td>0.455670</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa2d6303-801e-4b77-850b-149e5541def1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aa2d6303-801e-4b77-850b-149e5541def1 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-aa2d6303-801e-4b77-850b-149e5541def1');\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-bbd89397-ec70-49af-8cbd-cc2c5852450f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbd89397-ec70-49af-8cbd-cc2c5852450f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bbd89397-ec70-49af-8cbd-cc2c5852450f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "features.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QoyjpnHuYjXl",
        "outputId": "7bafe358-1e3f-4958-fabf-e011ecace72a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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4opuqdmqkUt6eSk9OVfCh89o9c7mi/YMlSSXLu2jwns9yPXbDS1/Id8P+/GhGgfnp1w36ftkqRURGqYZ3FU18bZjqPlA917Jp6en6dslKrdm0XWERV1W5QjmNGv68HmnSwFwmIyNDc7//Seu37FREZLRcXUqrx+Nt9OKA3rKwsCioZhWIKVNGadCgZ+Xs7Ki9ew9q5MiJ8vMLzLN8iRLFNXXqm+revaNcXV109OhJvfnm2zp06HiOcjVqVNX06RPUsmUTWVtb68yZ8+rb90VdvHgln1uU/6ZOfVNDBmf12Z49B/XKyAny9Q3Is3yJEsX1zttj1b17J7m5ldHRo6c0atQUHTyUPYYWL+6gGdMn6oknOqlMGWcFBF7UnC8X6OtvFhdEk/LVT6s2auHy1YqIjFYN78qa8OoLqlurWq5l09LT9e3SX7V2yw6FhUeqcoWyeuPF5/RI4/o5yoWGX9WnXy/W7v2HlZycqgrlPPTeuFdUu0bVgmjSfeXg0RP6/seVOn3WV+FXI/X5zLfU9tHmhV2tQrFs059auHa7IqJjVb1SOU0Y/JTqVquUZ/nFG3Zoxea/FBIRJWfH4mrftJ5ee7ab7GxtzGVCr0brs6VrtfvIaSWnpKmCh4umjein2t4VC6JJBab1qF56uO9jKuZYXBcP+ui3SQsUeYv7jgb926ph/3ZyLu8qSQo/f0l/fr5Kvtd9NxiwbJIqN3sgx3EHl/yu3yYtyJ9GFIK3bxgPRtzheNDjuvHgjRvGAzc3F82cMUnt2z0qZ2cn7dr1t157461bnhd3xlTYFUC++c/N1Hl0eDc1H9RRqyct0Nwebyk1KVmDF42XtZ1NnsfEBEdq8/vL9GW3yZrzxGT57Tml574eLbdq5cxlnvn4Jbl4ldWiFz7WZx3H69SmA3p2zmvyrJ33YHo/cu76iMpOHqKQz5fpXNc3lHQmUF6L35F1Gadcyzs0qKnKs9/U1RVbda7L64rZsk9Vvp6oYtWzB/vkgMu6NOUrneswUr69xin1Upi8F78jq9KO5jLxe08ocMQHOtPmJQUMnyW7Sh6qMn9cvre3oHm98oQqv9BJJ8d+qz2dJysjMUWNl0+Q5S3ef9YOxRR3Kkinxn9fgDUtPA1e6qp6gzpo+4QFWv7EVKUlpqjHknGyukUflWtSS8d+2KrlPd7Wqn7vy9LaWj2XjJO1vZ0kKf7KVX3TYESObe/HK5Uan6SgHcfyPK8RbPx9lz6Y851eGthHP3/7qWpUrawX35yqq1HRuZaf/c0S/bx2kya+NkxrFs3RM9076bVJM3XGx89c5rsff9HyNRs18Y0XtXbxHI0a/rwW/LhKS39ZX0CtKhijR7+kl18epJEjJ6hlyyeUkJCo9euXyM7OLs9j5s37QG3bttTgwa+rQYP2+v33Xfrttx9Vtqy7uYyXVyVt3/6Lzp3zU4cOvdWoUUfNnPmFkpNTCqJZ+erNN1/WKyMGa8Qr49XikW5KSEzUhvVLb9lnX331kdq2a6mBg17Vw/Xbaeu2P7Rp0zKVLethLvPRh1PVoUNrPT9wpOo+2Fqzv/hWn3/+nrp2bV8Qzco3m7bv1ofzvtfw55/Riq8/UnXvynpx7Lt5X5/f/aiV67dowsgXtHrh53rmiY56/a0PdOa8v7lMTFy8BoycKGtrK82b9ZZWL/xcY14aKMcSJQqoVfeXpKRk1ajqpUmjXy7sqhSqTX8d1oc/rNLwpztp+ftjVKNSOQ2fPldXY+JyLb9h10F9vnSdhj/dSas/m6h3XuqrzXsO64sf15nLxMYn6vm3PpO1lZXmTnxJqz6dqDef7yHH4vYF1awC0Xx4VzUe2FEbJn6v77pPUVpiivotHn/L+4644Ej9/v4yfdN1kr7pNlkBe06p9zej5HrddwNJOvTjdn3c8GXztm3mT/ndnAIz5tp48PIr49X82njw223Gg6+/+kjtro0H9a6NB5tvGA9+XblAXlUq6sleg9WwcUcFXbiszRuXycGhaL3vgHvpPxfUaTG4k3bMXq0zWw8p5OxFrRg1TyXdnfVAh4Z5HnP298M6t/OorgaGKCIgRFs+WqHUxGRVfDj7l7aKDapr7w+bdemYn6IuhmnHl6uVHJugcnWqFESz7hnXF7rr6rItivz5d6Wcv6hLE+fKlJSi0s+0y738oG6K/eOwwr9apRTfSwr5eKmSTvrL5fku5jLRa/5U/F/HlHoxVMnnL+rytO9k5Vhc9rUqm8uEf7dWiUfOKe1yuBIPnVXo3F/k8HANydoqv5tcoCoPe1y+n65S2KZDijt9QcdemSM791Jyfzzv91/49qPymbVCoRuNN+vr33h4SCftn71G/lsPK+LsRW15Y76KuznLu0ODPI9ZM+ADnVm5S5E+lxVx5oK2jv5KjuVd5Fa3siQp05SpxPCYHJt3x4Y6v36f0hKN/UV70Yo1eqprB/Xs3E7elStqyuiXVayYnVZt2JZr+XVbdmpo/6f1aLOGqlDWQ316dFbLpg20cPlqc5mjJ8/qsRZN1KpZI5XzdFeH1i3UvFE9nTjjk+s5jeqVV4Zo1qzZWr9+q06ePKshQ96Qp6ebnniiQ67lixWzU8+ej2vixBnavXu//P2D9N57n8rPL0jDhj1nLvf222O0efMOTZo0Q8eOnZK/f5A2bNiq8PCrBdW0fPPqyBc0Y+bnWrdui06cOKNBg15T2bLu6t69Y67lixUrpid7dtaECdO1e/c++fkFatq0T+TnF6gXXxxgLte0WUMtXrJSf/65V0FBl/Ttd0t1/PhpNWr0cEE1LV8s+nmdenVpr56Pt5V35QqaMupF2Rez06qN23Mtv37rH3rh2V56tGkDVSjrod7dO6llk/r6YcVac5kFP62Sh5uL3hs3UnVrVVN5T3c1b1RPFcp55HrOoq5ls0Z6ddjzateqRWFXpVAtWr9Dvdo2V4/Hmsq7gqfeGvaM7G1ttXr737mWP3YuQPVqeKlLy4Yq51ZGzR+qpcdbNNBJ3wvmMgtWb5N7GWdNG9FPdatVUnn3rHIVPFwLqlkFosmQTtr15Wr5bD2ksLMXtXrUPJV0c1bNW9x3+Px+RL47jikyMFSRASHa8eHPSk1MVrn6OWfLpSWlKCE8xrylxifld3MKzI3jwcC7GA92XRsP3p32iXz9AjX82nhQrZqXmjZtoBEjJ+jgoWPy8fHTiFfGy96+mPr07lGArQOM5f8K6mRmZmrHjh365ptvtH79eqWlpd2reuWLUhXc5OhWSr5/nTTvS4lL0sWjfqpYP/ep0DeysLTQg92aydbeThcOZz+SdOGQjx7s2lT2TsVlYZFVxtrORgF/n7nn7cgvFjbWcqhbVfG7j2bvzMxU/O5jKl6/Zq7HFK9fU/G7c850iPvzcJ7lLWysVebZjsqIiVfS6dynUVo5lVCpHq2UcOislJ7xr9pyP7Kv5KZi7qUU8ecJ8770uCRFH/aVc8PcH5X5r3Gs6Kribs66sDv7Gk2NS1LIUT95NLiza1SSbEs6SJJSohNyfd2tbmW51amsU8v/+P8qXMjS0tJ02sdXTRvWM++ztLRU0wYP6dips7kek5qWJlvbnL8+2tnZ6siJ7M+qenVqat/h4wq8eFmSdNY3QIdPnFbLJnnf4BpNlSoV5enppu3bd5v3xcbG6cCBo2qSRzutra1lbW2tlJScgcDk5GQ1b95IkmRhYaHHH2+j8+f9tW7dYl24cFh//rlG3brlHigykqw+c7+pz/bvP6KmefaZlaytrW+apZSUlKwW1/pMkv7ee1DdurY3/1rbqlVzVavmpa1bjXuNZl2ffmra4EHzPktLSzWt/6COnTqX6zGpaWk5Hn2Rbr4+d+45oAdqeGvU2x+qVc+BenroaK1cvzV/GgFDSEtL1xn/i2r6YA3zPktLSzV5sIaO+eR+r/VQjSo6439RJ84HSZIuhUZo15HTeqR+9uNCOw+eUG3vihr98QK1GjJRz4x5Xyu37cnfxhQw5wquKulWSv67T5n3pcQl6fJRP5W/i+8Gtbs1lY29nS4d9s3xWt0eLfTmkfkavmWW2oztLetitve0/oXln/Hg93swHiRfNx7Y2WX1z/VlMjMzlZKSqhYtGt/rZgBFxl3l1OncubN++uknOTk5KTIyUp07d9b+/fvl4uKiq1evqnr16vrzzz/l6np/RvBLumY9QhQfHpNjf3x4jPm1vLjXqKCXfn1H1nY2Sk1M1pIXP1WY72Xz6z++8oX6fvmqphz7Rhlp6UpLStWSFz/V1aC8n8e931iVcpSFtZXSIqJz7E+LiJadd7lcj7F2dc61vLVrqRz7HNs0VKUvx8jS3k5pYVHy7T9FGVE5pwR7jn9eLs93kZVDMSUcPiv/QdP+7zbdT+xcnSVJqTe8/1LDY2Tn5lzwFboPFb/WR4kRsTn2J0bEqvhtrlEzCwu1eru/rhw4p6s+l3ItUrt3a109f1nBh/LOFWUEUTGxysgwqUwp5xz7y5R2VsCFy7ke06Lxw1q0Yo0aPlRHFcp56O9Dx/T7n3uVYcp+0vqFfk8pISFJ3fq/LCtLS2WYTHp1aH917dA6H1tTsNzds8apsLCIHPtDQyPMr90oPj5Be/ce1IQJr+rsWV+Fhoard+/uatKkvjkPj5ubi0qWLKE333xZb7/9oSZNmqkOHVpr+fKv1bFjb+3atS9f25WfPNzdJEmhoeE59oeGRcjdwy3XY/7ps0kTX9PZs+cVGhquPn16qGnTBvK9LnfRa6+/pfnzPlBQ4CGlpaXJZDJp+EtjtXu3cfsrKiZOGaZcrs9SeV+fzRs+rEU/r1ODhx5QhbIe+vvwcf2+6+8c1+elK6FasWazBjzdTUP79dLJs76aNfs72Vhbq3unx/KzSbhPRcUlZL3XnErm2F/GqaQCLud+H9qlZUNFxyXo+bc+k5Sp9AyTnm7fQkOfzA5AXwq7qhVbduu5ro/phSfb65TvBb2/4BfZWFupe+sm+diiglPi2v1XQsQN3w0iYlTi2j1JXtxqVNDgVW9nfTdISNaKFz9VxPnsa/vkmj2KuRyhuNBoudWqoHbj+6qMt6d+fvGze9yKgner8cDjDsaDM3mMB2fP+ioo6JKmvzdBL708TgkJiXr9taGqUKGsPPM4L+6cSZmFXQXkk7uaqbNp0ybzL5STJ09WXFyc/Pz8FBYWpqCgIBUvXlxTpky57XlSUlIUGxubY0vPvPczMup1b6G3Ty0wb1Y2//5Rngj/K5rdeYLm9piifUu26amPh8utanago/2op2Xv6KBvn52uL5+YrN3f/aa+c16Ve40K96Iphhe/94TOPf66zj85TnF/HFblueNuytMT9tWv8un8unz7TVFmhkkVP329cCp7j5Tt1UId/BeaN8v/4/1XVNXo0VwvnfnWvFneg8ftHnvveZWpXl4bR8zJ9XUrOxvV6N5Mp5bt/L//lhGNf3WoKpUvq27PvayH2z6pGZ99rR6Pt5OlRfZwsGnHbq3f+ofenzJaK779VNMnvq6Fy1ZrzcbfC7Hm/58+fXooIuKMebOx+XfrBAwZ8oYsLCwUEHBAsbG+evnlQVqxYo1M1750W1pm9eP69Vs0e/Z3On78tD76aK5+++13DR3a/561pyD07dtTUZE+5s36X/bZwEGvysLCQheCDishPkCvjBis5ctXm/tMkkaMGKTGTeqrR8+BatL0cY0d+66++Hy62rRpea+aYwjjRw5WxfKeeuL5V1W//TOa+cW36t6pTY7r05SZqVrVvfTa0P6qVc1LT3froF5d2mnFus2FWHMYzYFT5/Xtr1s0aejTWvb+WH365hDtOnxaX63cZC5jMmWqVpXyeu3ZbqpVpYKeat9Cvdo1089b/irEmv9/6vRorvGnvzNvVv/HfUeE/xV99fhEfdd9ig4u+V3dPx4ul+ty6hz+aYf8/jyhsHMXdXL1Hq0eNU+1OjVSqYrGC0707dtT0ZE+5u3fjqHPXxsPLgYdVmJ8gEaOGKxl140H6enpevqZF1Stmpciwk4rLsZXrVs118aNv+cYMwDk9K9Xv9q+fbs++OADVamSlTOmfPnyev/99zV06NDbHjtz5ky98847Ofa1cKqjls51/211cnV62yFdPJo9DdLKNqu5JVydFBcebd5fwtVJwaeDbnmujLQM86ybKycDVP5BbzUf3EmrJ36n0hXd1HxgR33afozCrkXoQ85cUOVGNdVsQHutNkiW+4yoWGWmZ8jGxTnHfhsXZ6Vf11/XSw+PzqN8VI59pqQUpQYFKzUoWIlHzqnWzvkq3bu9wuauvO7vxykjKk4pAVcU5HtRtfd9L4f6NZR4OPdp6ve70E2HFH0o+/33TzJkW1cnpYRFm/fbujop9tSt339Flf/Wwwo5kp2g18ou6xp1cHFU4nV95ODiqPDTF248/Cat3x2gKm0f1sqn31N8SO4rp1Xr0ljW9nY6+8vuXF83klJOjrKysrwp6erVyGi5lHbO9ZjSzk76YsYkpaSkKjo2Tm4upfXp/B9U/rpEvx/PXagX+vVS57aPSpKqe1dWcEiYvl26Ut0fb5tfzclX69dv1f79R8z//ieRo5ubi0JCwsz73d1ddOzY6TzP4+8fpPbtn5GDg70cHUsqJCRMixfPUUBA1vszIiJSaWlpOnMm5yyws2d91aJFo9xOed9at27LDX2WNS3e3d01Z5+5uejYsVM3Hf8Pf/8gtW33VI4+W7p0ngL8s/qsWLFiem/aeD319AvaeC1weOLEGT30UG2NeuNFbd++Kz+al+9KOZWUlWUu12dUtMrc6vp8b7xSUlMVHXPt+vx6scp7Zl+frmWc5V2pfI7jvCqV17ZduedOQdFXqmTxrPfaDUmRr8bEycW5ZK7HfLlsg7o+2ki92matFFa9UlklpaTq3a+WaeiTHWRpaSnXUo7yKp8zV1OVcu7a9rdxFxjw2XpYX11332F97btBcRcnxV9331HCxUkht/luYErLUNS17wbBJwNV9iEvNRnUURsm5n7ff/na3y1d2V1RF8JyLXO/upvx4OhtxoM2N4wHP143HkjS4SMn1LBRBzk6lpStrY0iIiK1Z/c6HbxhlUkA2e46p84/y9lGRUXJ29s7x2tVq1bVlSu3X651woQJiomJybE1c3rgtsfdrdSEZF0NCjVvYecvKzYsSt7Na5vL2JWwV4V63jny49wJC0sL80Bgc22FnUxTziltJpNJFhbGyUWdmZauxBO+KtHioeydFhYq0eJBJRzOPT9HwuGzKtHiwRz7Srasl2d5M0sLWdrmvaqALLPeZ7csc5/LSEhWYmCoeYs/d0nJoVFyaVnHXMa6hL2c61dV9MGilYD2TqUlJCsmKNS8RfpcVkJYtCq0yL5GbUvYy6Oet0Ju86hU63cHyLtTQ/3aZ4ZiL4bnWa5279by33ZYSZG5rwhiJDY2NnqgelXtu24pUJPJpH2Hj+uh2rnntfqHnZ2t3F3LKD0jQ1v/3KPHHsmeSp+ckiILy5xLl1taWcpkMu603fj4BPn7B5m3M2d8FBwcpscey06uWrJkCTVqVE/79h267fkSE5MUEhImZ2cntW//qNZfy2mSlpamgwePqXr1nONjtWpVdOFC7o8D3q/i4xPk5xdo3k6f9lFwcKgee+wRc5mSJUuoceOH9fdd9lmH9q207trMEhsba9na2t70K2xGhsk888mIsq5Pb+07nP1FxGQy6e/Dx/VQ7Rq3OFKys82+Prf9+bceuy4gWK92LQVezHmvFXjpijzzeGwQRZ+NjbVqeVXQvhPZ9xImk0n7TpzTQ9VzX7AjOSVVljd+zl+73jKvfdTXq+GlwCs5gw9BweHyvOEReyNJTUhWVFCoeQs/f1lxYVGqcsN9R7l63rr0L74bWN3ivtXj2oq4cdcFj4wir/GgzT0aD9bmMtMwNjZOERGRqlq1iho0eMg8ZgC42V3P1Bk4cKDs7OyUlpamgIAA1a6d/SEYEhIiZ2fn257Dzs7upuXurC0K5tGUvxZsUpuRPXU1MESRF8PVfvTTiguN1uktB81lhiydqNObD2rvoi2SpI5je+vczmOKvhIhu+L2qte9uao0raXvB8ySJIX7XVFEQIh6zhii32b8qMSoOD3QoaGqPlJHiwZ/VCDtulfCv12jih+/rsTjvko85iPXwU/I0qGYIn/O+vW04ievKy0kUsEfLMoq//06VVs+Q65Deyh2+wGV6vao7OtW1cXxWY++WNrbyf2VZxSzbb/SwiJlXcpRLs93kY17GUVvyJop4VCvuhweqqaEA6eVHhMvu0qe8hjdTymBwbcPDhlM4NcbVfWNnkoICFHShTBVG/eMUkKjFLox+/3XeOVkhf52QEELsgYvKwc7OVTJ/qXMvqKbStaupLToeCVfNv5qOjc68t0mNX61h6IDQxV7IUzN3nxKCWHR8tuSfZPw5E8T5LvpoI7/kPVF+rH3BqpG92Za98KnSk1IlsO1/DspsYnKSMlO4O5UyV3lmtTQmueNdV3eyoBnumvSzM9Uu0ZV1alVXUt+XqukpGT16Jw1o2bC9E/l5lJab7z4vCTp+OlzCg2/qprVvBQWflVzv/9JmaZMDe77pPmcrZs30jeLf5anu6uqVq6oM+f9tWj5GvXsnPsqeEb15Zffafz4V+XrG6jAwAuaOvVNBQeHae3aLeYyGzf+pDVrNmn+/B8kSe3aPSoLCwudP+8vb+/KmjFjos6d89MPP6wwH/Ppp19pyZI52r17n3bu3KMOHVqrS5d26tChd4G38V77Yva3mjjhVfn6+isw8KLefnuMrlwJ1Zo12Tfbmzct15o1GzV33kJJUvv2rWRhYSEfHz95e1fW+7Pe0rlzflr4w3JJUlxcvP74Y49mzZqspKRkXbhwSY+2bKb+/XtpzJh3C6OZ98yAp7tp0qzZql29qurWqqbFK9cpKTlFPTq1kSRNnPG53FzL6PVrj+YdP+2jsIhI1ahaWWERkZq3cLlMmZka1LfndefsqudemahvlqxUx8da6MSZ8/pl/VZNGTW8UNpY2BITk3ThUnaQ6/KVUJ318ZOTY8n/VA6OAV0f0+Q5S/SAdwXVrVpJSzbsVFJKqno8lhWwnzh7sdxLO+m1fk9Iklo1rKPF63eoZpXyqlu1si6GhGvOsg1q1aCOrKyygjvPdW2tAZM/1Te/blHHZg/rhG+QVm7bo6kvGv+z7Hr7vtukliN7KDIgRNEXw9V69FOKC4vW2evuO577cYLObj6oA9fuO9qM7S3fnccUc+27QZ3uzVW5aS0tfe59SVKpim6q06O5fLcfVWJ0vNxrVlSHKf0V9PcZhZ29WCjtvNf+GQ/OXxsP3sllPNiyablWXzcedLg2Hpzz8VNV78qadcN4IEm9enVVRPhVXbh4WXXq1NSnH7+rNWs3aeu2Pwu6iYBh3FVQZ8CAAeaZOt27d1diYmKO13/55RfVq1fvnlUuP/w5f51s7e3Uc+YLKubooKADPvr++VlKv+6LX5lK7nIonT1dtXgZRz3zyUsq6eqs5LhEhZy9qO8HzJLvtRV6TOkZWjjoA3Ua10cDvn1TdsXtdDUoVCtHz9e5nUcLuon/l+j1u2Vdxkmeo56VtWspJZ32l/+At5V+LRmybVlX6bpf6xMPnVXgqx/L881+8hzznFICryhg2Awl+2RNo8w0mWRXtbwqP9VG1qUclREdq8Rjvjr/9Hgln88a1ExJKXLq1Eweb/SVpX0xpYVHKW7nYQXNXq7M1PQC74P85P/lWlk52KnuR0Nl7eigqP3ndKDPLJmue/85VHKX7XXvP6d63mq6KjtX1QPvZi37eGnZHzr+2ryCq3wBOTRvvWzs7dR25mDZOTroykEfrX7ug5zBmYpusr+ujx4ckBVseOrnyTnOtWXUVzqzMvvRjdq9Wyk+OFJB161AZnSPt22pqOgYfbngR0VERqlmVS/N/+htuZTO+iU1ODRclhbZv8ampKZp9rdLdSk4RA72xdSyaUPNnPyGHEuWMJeZ+Powzf52qd77ZL4io2Lk6lJaTz/RSS8NLFo38h9/PE/Fi9t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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig,ax=plt.subplots(figsize=(15,15))\n",
        "ax=sns.heatmap(data=features.corr(),annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "UPjrS6BNYjXl"
      },
      "outputs": [],
      "source": [
        "def mdlscr(model,x_tr,x_t,y_tr,y_t):\n",
        "     model.fit(x_tr,y_tr)\n",
        "     print(model.score(x_t,y_t))\n",
        "     return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vxZx-9HYjXl",
        "outputId": "ab77c9e5-b5b4-4ed6-f19a-765f1cae60b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "random forest classifier\n",
            "0.9523809523809523\n",
            "Logistic Regression\n",
            "0.8571428571428571\n",
            "kNeighbour\n",
            "0.8571428571428571\n",
            "Decision Tree\n",
            "1.0\n"
          ]
        }
      ],
      "source": [
        "ans={}\n",
        "np.random.seed(42)\n",
        "x_train,x_test,y_train,y_test=train_test_split(features,labels,train_size=0.9)\n",
        "print(\"random forest classifier\")\n",
        "ans[\"RandomForestClassifier_all\"]=mdlscr(RandomForestClassifier(),x_train,x_test,y_train,y_test)\n",
        "print(\"Logistic Regression\")\n",
        "ans[\"LogisticRegression_all\"]=mdlscr(LogisticRegression(max_iter=1000),x_train,x_test,y_train,y_test)\n",
        "print(\"kNeighbour\")\n",
        "ans[\"kNeighbour_all\"]=mdlscr(KNeighborsClassifier(),x_train,x_test,y_train,y_test)\n",
        "print(\"Decision Tree\")\n",
        "ans[\"dtree_all\"]=mdlscr(DecisionTreeClassifier(),x_train,x_test,y_train,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "voting_classifier_hard = VotingClassifier(\n",
        "    estimators = [('dtc',DecisionTreeClassifier()),\n",
        "                  ('lr', LogisticRegression(max_iter=1000)),\n",
        "                  ('rf',RandomForestClassifier() ),\n",
        "                  ('Knb',KNeighborsClassifier() )\n",
        "                  ],\n",
        "    voting='hard')\n",
        "voting_classifier_soft = VotingClassifier(\n",
        "    estimators = [('dtc',DecisionTreeClassifier()),\n",
        "                  ('lr', LogisticRegression(max_iter=1000)),\n",
        "                  ('rf',RandomForestClassifier() ),\n",
        "                  ('Knb',KNeighborsClassifier() )\n",
        "                  ],\n",
        "    voting='soft')"
      ],
      "metadata": {
        "id": "6EguC3CpEWng"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " voting_classifier_soft.fit(x_train,y_train)\n",
        " print(voting_classifier_soft.score(x_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNS4nRoUE-H6",
        "outputId": "fb1d74b9-c068-48ae-c877-6556d4323ce7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " voting_classifier_hard.fit(x_train,y_train)\n",
        " print(voting_classifier_soft.score(x_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-iwsJExuFz5m",
        "outputId": "b4f81506-6f29-44f8-9eb2-3d2b3fe0e432"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluvate(model,x,y):\n",
        "    y_predict=model.predict(x)\n",
        "    print(classification_report(y,y_predict))\n",
        "    ConfusionMatrixDisplay.from_predictions(y,y_predict)"
      ],
      "metadata": {
        "id": "HSbEaKLxG7KV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluvate(voting_classifier_soft,x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "BILhVo0iHITl",
        "outputId": "5a220be0-adea-402f-dbe5-e815a1ddd203"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          no       1.00      1.00      1.00        11\n",
            "         yes       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           1.00        21\n",
            "   macro avg       1.00      1.00      1.00        21\n",
            "weighted avg       1.00      1.00      1.00        21\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluvate(voting_classifier_hard,x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "7ZcYskvqHl8W",
        "outputId": "9f054770-40d9-4c0c-b138-90e2664944bd"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          no       0.92      1.00      0.96        11\n",
            "         yes       1.00      0.90      0.95        10\n",
            "\n",
            "    accuracy                           0.95        21\n",
            "   macro avg       0.96      0.95      0.95        21\n",
            "weighted avg       0.96      0.95      0.95        21\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MTxAeqAvJeyj"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}