tkinter.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM6VURSUV0WP+ZaQyL8+yVI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ranimansi/DSDL-2022/blob/main/tkinter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B6w-MgqJZgLe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sac=pd.read_csv('/content/heart.csv')"
      ],
      "metadata": {
        "id": "37LlOTQEaB8d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sac.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gaYO8stjaSxd",
        "outputId": "d9faa92a-1c84-41e4-9bb5-6cd11502e902"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0              N      0.0       Up             0  \n",
              "1              N      1.0     Flat             1  \n",
              "2              N      0.0       Up             0  \n",
              "3              Y      1.5     Flat             1  \n",
              "4              N      0.0       Up             0  "
            ],
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
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              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>172</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
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              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "puHkRBBhaVma"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "xY5XnZ1qanLv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='RestingBP',y='Cholesterol',data=sac)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "PH6vV9TQa5Xy",
        "outputId": "b8b59cab-7cac-4205-fa37-4d3610825c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=sac[['RestingBP']]"
      ],
      "metadata": {
        "id": "blNOPBw_bN0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=sac[['Cholesterol']]"
      ],
      "metadata": {
        "id": "CKJfu2b0bj8c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "G8kG9SWCbsCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)"
      ],
      "metadata": {
        "id": "HOEw1fGZcHBx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hU7phY8TcmvI",
        "outputId": "7638bd1b-14f5-4ca4-8ff9-5fef3486fbb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Cholesterol\n",
              "170          243\n",
              "200          249\n",
              "462          233\n",
              "72           182\n",
              "550          260"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b678c84a-0887-4ee1-ad8f-15d580054ee7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>243</td>\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b678c84a-0887-4ee1-ad8f-15d580054ee7 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-b678c84a-0887-4ee1-ad8f-15d580054ee7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XRafnUkedJtM",
        "outputId": "553b0847-b0c1-4d1b-8791-5da20e9b3a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Cholesterol\n",
              "536            0\n",
              "342            0\n",
              "268          242\n",
              "778          259\n",
              "8            207"
            ],
            "text/html": [
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              "      <th>Cholesterol</th>\n",
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              "      <td>259</td>\n",
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              "      <th>8</th>\n",
              "      <td>207</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1a7bf51-143a-49ea-acfc-888f9704fed2')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
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              "          document.querySelector('#df-d1a7bf51-143a-49ea-acfc-888f9704fed2 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-d1a7bf51-143a-49ea-acfc-888f9704fed2');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2XXuh7KpdPr7",
        "outputId": "3a0f77db-e387-49d5-e9e7-0d755181336d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     RestingBP\n",
              "170        120\n",
              "200        110\n",
              "462        122\n",
              "72         120\n",
              "550        172"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70f341c8-830d-4802-bcab-189386dd0590\">\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>RestingBP</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>120</td>\n",
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              "      <th>200</th>\n",
              "      <td>110</td>\n",
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              "      <th>462</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550</th>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70f341c8-830d-4802-bcab-189386dd0590')\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-70f341c8-830d-4802-bcab-189386dd0590 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-70f341c8-830d-4802-bcab-189386dd0590');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LQHMvtMtdWN3",
        "outputId": "07a8c33f-c8f4-4178-d12a-798a4bcd58b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     RestingBP\n",
              "536        133\n",
              "342        110\n",
              "268        130\n",
              "778        128\n",
              "8          140"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a84fc410-14a1-4e6b-8fa5-431ab2ef2554\">\n",
              "    <div 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>RestingBP</th>\n",
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              "      <th>536</th>\n",
              "      <td>133</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>778</th>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a84fc410-14a1-4e6b-8fa5-431ab2ef2554')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a84fc410-14a1-4e6b-8fa5-431ab2ef2554 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-a84fc410-14a1-4e6b-8fa5-431ab2ef2554');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "CX0JnkiXdcmP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LinearRegression()"
      ],
      "metadata": {
        "id": "x74YTDzDfRAN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVGPsaJYkN_-",
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          "execution_count": 20
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          },
          "metadata": {},
          "execution_count": 22
        }
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      "execution_count": null,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "     RestingBP\n",
              "536        133\n",
              "342        110\n",
              "268        130\n",
              "778        128\n",
              "8          140"
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n"
      ],
      "metadata": {
        "id": "IZRZqFvZkdoT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOop4qQRkwif",
        "outputId": "da04cb25-ccc6-413a-a810-ed49475f53e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "323.1463851660407"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model 2"
      ],
      "metadata": {
        "id": "9ddAob0ylXVc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=sac[['RestingBP']]"
      ],
      "metadata": {
        "id": "8CjBww9llbXk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=sac[['Cholesterol','MaxHR']]"
      ],
      "metadata": {
        "id": "VnQFZsWQlm5q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)"
      ],
      "metadata": {
        "id": "V0S4xV3tlwQu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr2=LinearRegression()"
      ],
      "metadata": {
        "id": "8VTqFzZkmRgK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr2.fit(x_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0VSS8QemWAO",
        "outputId": "f7e2af4a-7990-4dc4-ae1a-eb0c56e7d03f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lr2.predict(x_test)"
      ],
      "metadata": {
        "id": "uBDh-cS6mckH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyGXZJbCmjpX",
        "outputId": "b18ae284-ffdd-42e0-821d-ede1a7040c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "296.67411743339756"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model3"
      ],
      "metadata": {
        "id": "YhMZT1bYmrk7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "iqmq4JnxfrPM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sac.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O29G4PnDh0CU",
        "outputId": "766c28e1-05cc-4c7f-e90e-9933c77b1552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(918, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
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        "sac.info()"
      ],
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        "id": "Mno3tzWIiCHZ",
        "outputId": "646cd60e-371d-444d-c9ad-b855f212ada6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sac.isnull().sum()"
      ],
      "metadata": {
        "colab": {
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        "id": "eLxS9KdVi-8E",
        "outputId": "ae694f2b-a69d-4d43-b9c6-b6401bc6a100"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Sex               0\n",
              "ChestPainType     0\n",
              "RestingBP         0\n",
              "Cholesterol       0\n",
              "FastingBS         0\n",
              "RestingECG        0\n",
              "MaxHR             0\n",
              "ExerciseAngina    0\n",
              "Oldpeak           0\n",
              "ST_Slope          0\n",
              "HeartDisease      0\n",
              "dtype: int64"
            ]
          },
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          "execution_count": 43
        }
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
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        "ExerciseAngina=pd.get_dummies(sac['ExerciseAngina'], drop_first=True)\n",
        "ExerciseAngina.head()"
      ],
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              "   Y\n",
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    {
      "cell_type": "code",
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        "new_data=pd.concat([sac,ExerciseAngina,Sex], axis=1)"
      ],
      "metadata": {
        "id": "pHOcQZLcl7k-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.head()"
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      "execution_count": null,
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            "text/plain": [
              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  Y  M  \n",
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              "                                                     [key], {});\n",
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              "\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",
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          "metadata": {},
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    {
      "cell_type": "code",
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        "new_data.drop(['Sex','ExerciseAngina'], axis=1, inplace=True);"
      ],
      "metadata": {
        "id": "YpoNvWtrmVQr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "new_data.head()"
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          "height": 206
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        "id": "-3HaFl8KoJGS",
        "outputId": "bd4311d6-847a-4704-d5d6-23b676137d23"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "   Age ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40           ATA        140          289          0     Normal    172   \n",
              "1   49           NAP        160          180          0     Normal    156   \n",
              "2   37           ATA        130          283          0         ST     98   \n",
              "3   48           ASY        138          214          0     Normal    108   \n",
              "4   54           NAP        150          195          0     Normal    122   \n",
              "\n",
              "   Oldpeak ST_Slope  HeartDisease  Y  M  \n",
              "0      0.0       Up             0  0  1  \n",
              "1      1.0     Flat             1  0  0  \n",
              "2      0.0       Up             0  0  1  \n",
              "3      1.5     Flat             1  1  0  \n",
              "4      0.0       Up             0  0  1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f72cee36-9db8-4bf8-bac0-6d70135fcae0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>Y</th>\n",
              "      <th>M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>172</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f72cee36-9db8-4bf8-bac0-6d70135fcae0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f72cee36-9db8-4bf8-bac0-6d70135fcae0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-f72cee36-9db8-4bf8-bac0-6d70135fcae0');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train,y_test=train_test_split(x,y, test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "ZjG6daqmoO7i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('x_train',x_train.shape)\n",
        "print('x_test',x_test.shape)\n",
        "print('y_train',x_train.shape)\n",
        "print('y_test',x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04z0654kpaJ-",
        "outputId": "b586515e-5d55-455c-b73b-7896b97d267b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train (642, 2)\n",
            "x_test (276, 2)\n",
            "y_train (642, 2)\n",
            "y_test (276, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier=RandomForestClassifier(n_estimators=5,criterion='entropy',random_state=0)\n",
        "classifier.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdD8LOdup-5u",
        "outputId": "bf0b3f5b-a47e-4fe7-ab6c-0ae35f5fed12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-72-c2c714061500>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  classifier.fit(x_train,y_train)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=5, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=classifier.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crp4ZblSqv9a",
        "outputId": "a493e972-9b3e-4257-aab0-b132b3d310f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0, 160, 120, 145, 125, 140, 120, 110, 160, 110, 132, 180, 120,\n",
              "       141, 120, 105, 130, 135, 130, 110, 141, 180, 160, 140, 120, 128,\n",
              "       112, 145, 130, 141, 142, 120, 120, 110, 110, 160, 110, 120, 160,\n",
              "       105, 136, 128, 139, 140, 120, 150, 160, 160, 130, 120, 128, 150,\n",
              "       140,  80, 170, 112, 156, 120, 160,  94, 112,   0, 110, 100, 125,\n",
              "       112, 120, 120, 100, 150, 130, 120, 110, 120, 160, 112, 115, 146,\n",
              "       100, 100, 125, 128, 110,  95, 110, 140, 152, 110, 110, 110, 140,\n",
              "       140, 140, 110, 110, 120, 131, 120, 122, 130, 140, 118, 180, 160,\n",
              "       160, 120, 130, 130, 140, 160, 135, 120, 130, 135, 142, 130, 110,\n",
              "       130, 120, 140, 139, 100, 144, 110,  94, 110, 120, 130, 100, 130,\n",
              "       120, 130, 100, 133, 130, 112, 120, 100, 124, 120, 120, 132, 105,\n",
              "       150, 125, 140, 130, 130,   0, 130, 112, 150, 140, 160, 120, 112,\n",
              "       128, 120, 130, 120, 112, 124, 132, 115, 132, 120, 141, 130, 160,\n",
              "       150, 125, 128, 100, 125, 105, 140, 140, 122, 128, 133, 120, 117,\n",
              "       130, 160,   0, 134, 155, 120, 130, 140, 115, 150, 160, 130, 128,\n",
              "       140, 140, 140, 160, 132, 110, 142, 106, 140, 160, 112, 130, 120,\n",
              "       131, 130, 130, 110, 136,  95, 115, 120, 106, 125, 110, 128, 140,\n",
              "        95, 150, 120, 141, 130, 120, 120, 140, 100, 125, 160, 120, 130,\n",
              "       120, 140, 108, 130, 136, 145, 124, 136, 150, 125, 120, 110, 120,\n",
              "       110, 112, 150, 150, 140, 160, 130, 110, 120, 106, 112, 133, 160,\n",
              "       110, 154, 145, 130, 104, 142, 125, 150, 100, 125, 130, 120, 150,\n",
              "       134, 160, 110])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report,accuracy_score"
      ],
      "metadata": {
        "id": "GjOfpVx8rIFk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm=confusion_matrix(y_test,y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BWf8o3LvrdOp",
        "outputId": "c20f3cbb-a0f4-4223-edf3-59ac5c8bae55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4T4zXEsroPJ",
        "outputId": "dcc14657-f2aa-4f9d-e027-c5ec7e19ec4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04710144927536232"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model4"
      ],
      "metadata": {
        "id": "gTGvsrZasPrx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tkinter"
      ],
      "metadata": {
        "id": "iOeoQVJ-sJYA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window = tkinter.Tk()\n",
        "window.title(\"Tkinter\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "K_trJmy3sTWK",
        "outputId": "0930f68f-9547-4c79-eeca-2ae0abf6b372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TclError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTclError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-1bd82469f618>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwindow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtkinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tkinter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/tkinter/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, screenName, baseName, className, useTk, sync, use)\u001b[0m\n\u001b[1;32m   2259\u001b[0m                 \u001b[0mbaseName\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbaseName\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2260\u001b[0m         \u001b[0minteractive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2261\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tkinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscreenName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbaseName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minteractive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwantobjects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0museTk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2262\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0museTk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2263\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loadtk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTclError\u001b[0m: no display name and no $DISPLAY environment variable"
          ]
        }
      ]
    }
  ]
}