ML_assignment.ipynb

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        "<a href=\"https://colab.research.google.com/github/Anyaaa-2/pesuio_assignments/blob/main/ML_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
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      "source": [
        "#importing dataset\n",
        "dataset = pd.read_csv('SwedishMotorInsurance.csv')\n",
        "dataset.head()"
      ],
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              "   Kilometres  Zone  Bonus  Make  Insured  Claims  Payment\n",
              "0           1     1      1     1   455.13     108   392491\n",
              "1           1     1      1     2    69.17      19    46221\n",
              "2           1     1      1     3    72.88      13    15694\n",
              "3           1     1      1     4  1292.39     124   422201\n",
              "4           1     1      1     5   191.01      40   119373"
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    {
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      "source": [
        "#.loc method to select specific columns\n",
        "data_ = dataset.loc[:,['Insured','Claims']]\n",
        "data_.head()"
      ],
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        "id": "FqyheOUTBoID",
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      "execution_count": 48,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Insured  Claims\n",
              "0   455.13     108\n",
              "1    69.17      19\n",
              "2    72.88      13\n",
              "3  1292.39     124\n",
              "4   191.01      40"
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              "      --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-67960f7b-c100-4927-a772-dbc00f89465d 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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(dataset['Insured'])\n",
        "y = pd.DataFrame(dataset['Claims'])"
      ],
      "metadata": {
        "id": "CJQWodwhnW_z"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#encoding the independent variable\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))"
      ],
      "metadata": {
        "id": "a9jNVt0BFz9P"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#encoding the dependent variable\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "CMKRusMPF9G-"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.plot(x='Insured',y='Claims',style='o')\n",
        "plt.xlabel('Insured')\n",
        "plt.ylabel('Claims')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "R8ZwzUExB_7P",
        "outputId": "2de2baef-1583-4ec8-8ccc-bd44e32d15a2"
      },
      "execution_count": 42,
      "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": [
        "dataset.plot(x='Claims',y='Payment',style='o')\n",
        "plt.xlabel('Claims')\n",
        "plt.ylabel('Payment')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "CKtOTy1OFPZK",
        "outputId": "032cd91d-1d51-41e4-bb99-5df2782a5186"
      },
      "execution_count": 53,
      "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": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "lJ4BBPzcCngh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "a0549b32-5ed6-4753-a4f3-a54e1052ee0a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-aa45bcd97fcc>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[1;32m   2557\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"At least one array required as input\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2559\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\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   2560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2561\u001b[0m     \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_make_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\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    444\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    392\u001b[0m     \"\"\"\n\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m     \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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    395\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    392\u001b[0m     \"\"\"\n\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m     \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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    395\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_num_samples\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    333\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"shape\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m             raise TypeError(\n\u001b[0m\u001b[1;32m    336\u001b[0m                 \u001b[0;34m\"Singleton array %r cannot be considered a valid collection.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             )\n",
            "\u001b[0;31mTypeError\u001b[0m: Singleton array array(<2182x2070 sparse matrix of type '<class 'numpy.float64'>'\n\twith 2182 stored elements in Compressed Sparse Row format>, dtype=object) cannot be considered a valid collection."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTt0dRDNDM2F",
        "outputId": "e50b04a7-0826-4bdd-c631-dc09b4b0fb93"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1745, 1)\n",
            "(437, 1)\n",
            "(1745, 1)\n",
            "(437, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "l3Cfq_EkDlsu",
        "outputId": "7befa3df-1c98-4783-fa41-49fd1f90892c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 56
        }
      ]
    }
  ]
}