Iris_Preditcion_using_Linear_Regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/Netesh5/Machine_Learning/blob/main/Iris_Preditcion_using_Linear_Regression.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QphacpFzhf4j",
        "outputId": "34677295-4002-4461-a7d9-a44e01cf883f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/mydrive/; to attempt to forcibly remount, call drive.mount(\"/content/mydrive/\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/mydrive/\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_squared_error as ms\n",
        "\n"
      ],
      "metadata": {
        "id": "gCt_GTKXkd8z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv(\"/content/mydrive/My Drive/Machine Learning/python/Iris.csv\")\n",
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81s7--Fznzsm",
        "outputId": "ff1073c2-816b-4947-d4f4-b52d47b903f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset\n"
      ],
      "metadata": {
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          "height": 424
        },
        "id": "VrQf4aemotFc",
        "outputId": "140a4fd9-a928-49cf-9e7d-e0988caeb2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "0       Iris-setosa  \n",
              "1       Iris-setosa  \n",
              "2       Iris-setosa  \n",
              "3       Iris-setosa  \n",
              "4       Iris-setosa  \n",
              "..              ...  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  \n",
              "\n",
              "[150 rows x 6 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
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              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>150 rows × 6 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIidapGMouT0",
        "outputId": "9d432ef8-f229-4af3-9a4c-0db3fd71aa16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
            "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
            "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
            "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
            "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
            "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
            "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
            "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
            "max    150.000000       7.900000      4.400000       6.900000      2.500000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.plot(kind=\"box\",layout=(3,2),subplots=True, sharex=False,sharey=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "Rzw9hsu_pJPo",
        "outputId": "3599d059-1365-4603-8774-06f4f46e786b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                  Axes(0.125,0.653529;0.352273x0.226471)\n",
              "SepalLengthCm    Axes(0.547727,0.653529;0.352273x0.226471)\n",
              "SepalWidthCm        Axes(0.125,0.381765;0.352273x0.226471)\n",
              "PetalLengthCm    Axes(0.547727,0.381765;0.352273x0.226471)\n",
              "PetalWidthCm            Axes(0.125,0.11;0.352273x0.226471)\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "yYAeCiaaqcqE",
        "outputId": "166c4919-cfd1-4ab3-e06f-0974c1ba16e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Id'}>,\n",
              "        <Axes: title={'center': 'SepalLengthCm'}>],\n",
              "       [<Axes: title={'center': 'SepalWidthCm'}>,\n",
              "        <Axes: title={'center': 'PetalLengthCm'}>],\n",
              "       [<Axes: title={'center': 'PetalWidthCm'}>, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=dataset.drop(columns=[\"Species\"])\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8VorMR77rAj8",
        "outputId": "0b434e13-8f19-4879-d02b-a14301740f5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "0      1            5.1           3.5            1.4           0.2\n",
              "1      2            4.9           3.0            1.4           0.2\n",
              "2      3            4.7           3.2            1.3           0.2\n",
              "3      4            4.6           3.1            1.5           0.2\n",
              "4      5            5.0           3.6            1.4           0.2\n",
              "..   ...            ...           ...            ...           ...\n",
              "145  146            6.7           3.0            5.2           2.3\n",
              "146  147            6.3           2.5            5.0           1.9\n",
              "147  148            6.5           3.0            5.2           2.0\n",
              "148  149            6.2           3.4            5.4           2.3\n",
              "149  150            5.9           3.0            5.1           1.8\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79ca9159-d2fa-4d66-881a-9ceb169ab1fb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79ca9159-d2fa-4d66-881a-9ceb169ab1fb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-79ca9159-d2fa-4d66-881a-9ceb169ab1fb 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-79ca9159-d2fa-4d66-881a-9ceb169ab1fb');\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-eb91cd90-362a-4cb5-85c9-09cd8c1b4547\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb91cd90-362a-4cb5-85c9-09cd8c1b4547')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb91cd90-362a-4cb5-85c9-09cd8c1b4547 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y=dataset[\"Species\"].map({'Iris-setosa':0,'Iris-virginica':1,'Iris-versicolor':2})\n",
        "Y\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ik9UKbtEr7Hk",
        "outputId": "daa8830b-7279-4e61-bee3-aef9ce9ea57b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "145    1\n",
              "146    1\n",
              "147    1\n",
              "148    1\n",
              "149    1\n",
              "Name: Species, Length: 150, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)"
      ],
      "metadata": {
        "id": "rZetT0PPsO3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=LinearRegression()\n",
        "model.fit(x_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "2mvkFTahs2V9",
        "outputId": "f4db1971-6950-43b7-9570-5f78d06fcb13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction=model.predict(x_test)\n"
      ],
      "metadata": {
        "id": "iBY1dOGY9opL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score=ms(y_test,prediction)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8q5k5ZsBEL_n",
        "outputId": "33945fbd-5dcc-4bb1-b6ef-4591cd8e138e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.22192709343792624"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.scatter(\n",
        "#     y_test,\n",
        "#     prediction,\n",
        "#     c='red',\n",
        "# )\n",
        "# plt.xlabel=(\"length\")\n",
        "# plt.ylabel=(\"width\")\n",
        "# plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QB6GX-IwJgcG",
        "outputId": "db8d6a42-84ed-4e42-966d-1aa5eadc8fab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q7dzKCeyKAFU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}