Lin_Regression_Boston.ipynb

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    "colab": {
      "name": "Lin-Regression-Boston.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "cf4ywQmaPK9H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJgclltePK-H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_boston"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGEQxRBCPK-Z",
        "colab_type": "code",
        "outputId": "e6b78899-51fd-409f-913a-5d6ad465a76d",
        "colab": {}
      },
      "source": [
        "dataset=load_boston()\n",
        "print(dataset.keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AtTA08rPK-y",
        "colab_type": "code",
        "outputId": "add8d6ea-54dc-45e7-9b90-5c28872bc6d5",
        "colab": {}
      },
      "source": [
        "print(dataset.DESCR)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _boston_dataset:\n",
            "\n",
            "Boston house prices dataset\n",
            "---------------------------\n",
            "\n",
            "**Data Set Characteristics:**  \n",
            "\n",
            "    :Number of Instances: 506 \n",
            "\n",
            "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
            "\n",
            "    :Attribute Information (in order):\n",
            "        - CRIM     per capita crime rate by town\n",
            "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
            "        - INDUS    proportion of non-retail business acres per town\n",
            "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
            "        - NOX      nitric oxides concentration (parts per 10 million)\n",
            "        - RM       average number of rooms per dwelling\n",
            "        - AGE      proportion of owner-occupied units built prior to 1940\n",
            "        - DIS      weighted distances to five Boston employment centres\n",
            "        - RAD      index of accessibility to radial highways\n",
            "        - TAX      full-value property-tax rate per $10,000\n",
            "        - PTRATIO  pupil-teacher ratio by town\n",
            "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
            "        - LSTAT    % lower status of the population\n",
            "        - MEDV     Median value of owner-occupied homes in $1000's\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
            "\n",
            "This is a copy of UCI ML housing dataset.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
            "\n",
            "\n",
            "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
            "\n",
            "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
            "prices and the demand for clean air', J. Environ. Economics & Management,\n",
            "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
            "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
            "pages 244-261 of the latter.\n",
            "\n",
            "The Boston house-price data has been used in many machine learning papers that address regression\n",
            "problems.   \n",
            "     \n",
            ".. topic:: References\n",
            "\n",
            "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
            "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5pu7Q0MPK_2",
        "colab_type": "code",
        "outputId": "e00668e5-f044-47cd-c577-f2c293110273",
        "colab": {}
      },
      "source": [
        "print(dataset['target'].shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3ncO0-mPLA-",
        "colab_type": "code",
        "outputId": "ec34d735-4343-432d-80ae-04b3088d2420",
        "colab": {}
      },
      "source": [
        "print(dataset.feature_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
            " 'B' 'LSTAT']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIYkV0SRPLBO",
        "colab_type": "code",
        "outputId": "e3ffdcef-b81c-4770-87e0-8ed9594468a2",
        "colab": {}
      },
      "source": [
        "print(dataset['target'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
            " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
            " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
            " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
            " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
            " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
            " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
            " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
            " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
            " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
            " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
            " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
            " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
            " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
            " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
            " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
            " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
            " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
            " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
            " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
            " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
            " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
            " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
            " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
            " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
            " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
            " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
            " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
            "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
            " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
            " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
            " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
            " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
            " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
            " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
            "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
            " 22.  11.9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzFM_RqFPLBe",
        "colab_type": "code",
        "outputId": "736f4789-8dcd-4af3-b122-dd199837a307",
        "colab": {}
      },
      "source": [
        "print(dataset['data'].shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0GCKJ12PLBy",
        "colab_type": "code",
        "outputId": "249b0626-19cb-4a8c-a43e-7fb3cacc618b",
        "colab": {}
      },
      "source": [
        "data=pd.DataFrame(dataset.data,columns=dataset.feature_names)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  \n",
              "0     15.3  396.90   4.98  \n",
              "1     17.8  396.90   9.14  \n",
              "2     17.8  392.83   4.03  \n",
              "3     18.7  394.63   2.94  \n",
              "4     18.7  396.90   5.33  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CG5lRyjPLCE",
        "colab_type": "code",
        "outputId": "03e1e572-1665-4f98-82db-775bb2a5238b",
        "colab": {}
      },
      "source": [
        "data['Target']=dataset.target\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  Target  \n",
              "0     15.3  396.90   4.98    24.0  \n",
              "1     17.8  396.90   9.14    21.6  \n",
              "2     17.8  392.83   4.03    34.7  \n",
              "3     18.7  394.63   2.94    33.4  \n",
              "4     18.7  396.90   5.33    36.2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gehPnezGPLCS",
        "colab_type": "code",
        "outputId": "aa13347a-98f8-4423-9d35-8cebf3fc9175",
        "colab": {}
      },
      "source": [
        "X=dataset['data']\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXWZ5-OIPLCf",
        "colab_type": "code",
        "outputId": "3cde685c-67c9-4b87-a36f-8dc496263072",
        "colab": {}
      },
      "source": [
        "fi=0\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GkbFqrzPLCs",
        "colab_type": "code",
        "outputId": "479aeced-1fda-4a3e-dec1-8eafa84f10f1",
        "colab": {}
      },
      "source": [
        "fi=1\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcJ6PdxmPLC3",
        "colab_type": "code",
        "outputId": "1f527bd5-16fe-49b1-9820-41aa28069fc7",
        "colab": {}
      },
      "source": [
        "fi=2\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzA14mqKPLDG",
        "colab_type": "code",
        "outputId": "14ed82fa-4e9f-4ab9-af2f-9823ffd57e18",
        "colab": {}
      },
      "source": [
        "fi=3\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuSYddnXPLDZ",
        "colab_type": "code",
        "outputId": "587dd628-91ba-46ef-9a8b-2f6d58e23ee1",
        "colab": {}
      },
      "source": [
        "fi=4\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDoQ5L16PLDo",
        "colab_type": "code",
        "outputId": "871f5e18-bbb4-4b6f-985b-146a2e5fbf63",
        "colab": {}
      },
      "source": [
        "fi=5\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adpofmUcPLD_",
        "colab_type": "code",
        "outputId": "3e663e07-1d0e-440b-a3e2-f5000c6dba59",
        "colab": {}
      },
      "source": [
        "fi=6\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LV7RuYsOapfRWxdXAe6vCrcOXxXKtzGq1VmYttqzeK4UI5FHyI+mgi9Ph/Vpc2q5o3cTQqn62b76GnWOTCHBTQp/4LFaH7TplKajPpaGFthqzkAd7Dp5wo5SnZ3IK5FHyo5VBl0QI+AU/tO6K1m0UdeMway02TNtsh+mmqOah/sV9boRjzX6dJirAC04eS0e/ACrj0jUqRdTasiTLySVstdgO002r94jTD+JcO3VmmopAzVjHBk6dSTdhqwrwEpC3Rpf3/bOiqFpbWQlbLbbDdNPKPeL0g7h9plCh9HnRbVpSkemktkhDMyxzfaRd9rDVYjtWkd57xD0cPE4/iNtncg+lz5tO0ZLKPNAdOqUtHFrVjspcH1mVPWy11o5VnPP7cTMyxukHSfpM3qH0uVJm16c4SaHKQJnbIohWtaMy10eZy96IJG6wcT22irQnVHgBXlYHfn9SqJoxpR8sZW2LRrSiHZW5Pspc9kYkzcgYpx8UaU+o8AK8aDNeVOqSQhlDpSII5U4KVda2yIoy10eZy96IbnODFevEtPYwPDxsRkZG2na/PHE0cE0KpSj50gn7TyIyaowZ9r9feA08CXk1mP++najhKErZKJLJI206ToDn5RkQdt84iYayFvZF0kQalcX7GWgirSLjpM5tJW1A2uVJ2l+KND6i0nECfOfYpJt7IGjTIqtGamVXv9Gkk1Z54xx4m1VUWqOyeJ/X+aynWgFjEuXTLttALCP+1LmPjE6y/Qv5eVi1oryV1SW0owT46MQUO0Yn3dwD1YrUbVqk2Uh+IdHKrn6Y8E+zvN57TM/U2Prtfbx05BcLhGOWUWnNntf5zE3+M1tDoOmRblHKpEI9HlFPBPKmzs3bw6oVJaqsbpUdJcD3HDzB7FwNsHIw3zp8WV0jJG0kf2cOExJJbd5hwj/NTuXcY3qmRg34yeQp97O4eSuc+nj95DuJytdosvMm/wGwlfDIk2JQ+WFhcEejcqYp7Ecnptg1ZikVcVLKtnrPVsof50SgutS5OXt5tKJEZelWqfnAI9K/uI+KWGc4VivCukvPq/s8SSMFdeYwIZd0syRM+KfZqZx73PvEy/zgwHH7zMuFhw00u6ffxFERS8D6Vzve6+OEVvuT/9z24QEuPf9dkTt/UPnjTIRpr9Ju/1vrtwB2jBxm++ZrGtr8WzVZpVH+qPU1tCqd1Llp0YoSlZXTgeYDj8joxBRbH9vnnqI+VzNsfWwfay9ZEklwhBHUmZNOBI3uGyT80+5UQ6v6ueuG9/L8a28zM1ujWq1wy9DKOs2w2T299TE7W0MqtpFDJPCZwzpv2GTnr9u4QsEpv6P1Bv1mo/ZqtocSRFjb7jl4ghlbeAPMzJnAPZk0TVZxVlBphJY3U1raZbry3ufOj18R+VpvmaIqYHGeKWvTTMcIcKeinEHrDMCdY5N1le1tpCgNEdSZ4wpWRxNzfiPORk/aLlBRyt7ont76EDvC1ABzcws7Z5LOm9ak5YRP7xqb5Ouf3xDpN5vtoYR9J0yoblizlN6eiquB91YX/l7aiZTirKDihpbHFcZpaJ9RDoiA6CayVsuk2QgzYsOapfRUrYoyQAVrAO4YnWR2rrG3Q6OGCOvMcWbrrd/e5w5iR6iELX/boa20Mil468PJ8RLWOZN23lYnrSBBd+fHr4ikLTXaQ4l6L28f2f6FDQ1t4GknUoqzgooTWp5E8LWqfUY9IOLm9SszyybY6vezMs04dIwAByxjLNBTFT49fBkCbP/hoVBvh6gNkVSgOB3t32Zqde8Hxb5mYSvzaym7xiZ56/RZLlyyaIEwiTN5eOtj7SVLQr+XpPOmsQH3xsl36KkIc7V4qQuCzDeNyjU6McXrJ9+hp1phbi5YqDbrO3HqqNm1UcwISSfVOi+miIIv7r38dRw2Rv3vG+qPAexf3Md9Tx0IrKNWNeIk39//5mlX4VEBHsKegyeYte3fpmZYYW987QzJg5D10sYpk6N5O/RVhZs9gsF/bVq2srrNxopYJiWPy5d3Q62VySOKgIr6W83K0Sz4Z+fYpLvi6qlWuO2q1g8T9v+u14feW7+/fdVA5udfNjInBJUxzoqyGf2L+9yN75qJdjRYnHsFtX3YGPW/f/N6ax/HuyoM60OtmocaPVPQbzy49xBf/uaLAPzTK8cBuOPqgaZ1F5WOEeBhtuotm9bx+PgRNg4uj9SQWZWpWrFOJw8b5GlPKHUTwpxZoPVPezbUiuID26gcUYJ/nM1HsGzyK85/V+zn8O+R+H/X65rolHWuZrg0wb3CCNP2G5kTgsqY5opy6sy065NfIfrRYM3u5TzrGx6X1LMzNe594mXuuuG9sQ6IGFrVz31PHWjal/1tHHXya/RMYe3z+PiRuuseHz+iAjyIMO3JmY2ff+3tOo8U5ztZCSqnYzZKYtUodwoQugyMgn/ymK0ZV4MCqAqRXQfbRaNy+JfwzgD3TkDO4/ldI5PS7HezqLMwQRA0uQHc+8TLdcLbW0ZvPvqoidTCNNENa5ayqDfd531w7yG27B5nrmaoVnD7pwGefeU4z7/2Nl///IZAc1DY2I3Tl5NMfmGEKR8bB5e7mjfAxsHlsX63GR0jwGFho+alWUYxSTTKnZKGPdw/Iex/8zR/6g4W4b/fONhUo0mjHuL8ZqPlrWNrnp2z2vPZV46z9+AJbh2+jHWXnhd5pROn7G+cfMf1SQeoVIQtm9a5v5vmhOsQ1mf9gql/cZ/bRxytuKcqXLf2Yi5csoj9b55m62P7XOFUEVry0Ei7j4xOTLFl9zizduX6LI2x3Di9xClnnIk/qeulo207VoA0tW/oMAHuJy/NMsrEEaZR+ZeTrUw83gltaFV/0w1HZ/JIQxBFncSaeff4bfm/vOI8Xpg8ZYXazxke3HuIRb3ppuv1aoYVx88dqNUM42+ccq9Lc8J1COuzfsHk7T8VgWuvuJCNg8vdFWfF4+IJuKaJMA8oaNwnvW64abDn4AlqnlTWVYFqtcLsrBUpXJHkqyi/iSSsz0c1cbbiegmWEE9bcDt0tABvh507iCgTRyONqqciDT0bkhLFHpnUX91Ps0nMKyQX9YYLPe/vzNUMgyvOY//R065m6WhqU2emmwZwRMGvGdZqhgpQs++1Y3RywcZomiu9Rn3W337e/nPXDe+tK4dziAjG4Ci3Bnhk5HDkfRhvn0w7itC5lzPZbL1xkLWXLGHn2CTHQzyl4hIlSVyUiT+p62U76GgBDvlUqjMIvdGAYdcEaVRzNcNtV13metK0q/y77OAXaOyvHoUgYeBo9kCdkJyeWbhh6dRLkGvfTetXuhtPaU9yfs2wIrb+bb8VNIDTXulF6bNhgt5bDkc4/fjwSZ746VE3QrmRAArrk0n9uOO4mD649xDfeP4wNWNc/+5WCCv/g3sPuebEvp4K93xynbvaiNIH/e2bZ6K0jhfgeeKPBmy0Gw71g69diY+8+CebVs5q8g5Qv2vXzetX1gtJT8RjkNYUtDm94vx3cc8n0z/lyK8ZXv++i/nuT4+6nxsWutC1e6UX5u/daA/hmZffctMnNJpgGvXJOBNTVLODt4x1k3rClYw3edigZ2/Eu7HrCG/nPn/6rRddX3K/i2hYH4zznFmiAjwj4moveZl7vNy8fiU7Rg4zM2foDfFXTxLw43ftOnb6bF3Ssa2eDdVdAblIvFGUWQ6YoGX1/jdP88RLR50YMYRgF7p2rfRGJ6wc3E4b+ZNjhZbDeYAGRyj627aVPhm3/+85eMIVqgAVaZ7GIKj83uRhfVXhnk8N1k3y9z11oO4+AHO+1RUsPAg5LJI3bxdcFeAZEbbsaraszENwe++/ffM1oZ4VSYWnf7PomZffcr1htt44yB1XD7j+uN8YOTyfiyRAW8xqwAQ9G8DWx/bVZW5c1BtfE01zUt45Nummb52eM+yMYObyBrmFmVAaeUUlKXdcs5Ljpjg9U6Pim9SjMDoxxb1PvFwXODczZxbsjXhXWGClK67KwqjdNNMbZIkK8IwI0l68fqde4VUkGnlWRBWeQULr5vUrMeCmN7D22awBFpRyQIBbhprnDmkUNh2HoGd7/eQ7nLXLVAGuvfJC1/c8ClEnvDhC3p/zcWEOyIVEETJJJ8YwX/O42nsr2n5Yyoqg5GFDqxbmp3Ge33vfqCkL8l45qwDPEL/2sufg/GkzszXDlt3jC4KLikLQgI4iCPxCa8umdXX27y2b1gXm6z4bMPiCTDiNbOtRVgSNAlX8E8NfP/mKuxroqUos4R1Wh400X683RphAuGn9Sh4ZnU8PcVOEjb4oQsa/Snrj5DuMTkw19Vpyyl4z8ysUr/bu1INTjmblbHa/oGfw9x8RuOH9y/hPH7s8sreIo7R4lYGwybaVs2/TpqkAF5HLgH8ALsHyptpmjPlrEbkAeBhYDbwGfNoYM5VdUcvPhjVL3ahIgJoJ9wjImyBh3UwQPLj3ENu+/2qdDfvx8SN1QmzqzHTgb3jrpVkmwDDberO6jBOosudg/MyE/ns1S3QF9UK+Zgx/+q0XLX/okLBuR4OMq/E1EzLO8zvePdt/eIidIZvvQWWHhcE3aaaTbTRZ+8eVAB+87PwFm42N6ixqWfO2efuJooHPAn9ojBkTkSXAqIh8F/g94EljzFdE5G7gbuBL2RW1/Ayt6mfrjYNs2T3uukq122bWiKClod8VMkwQeJP2wHxU28bB5e4BEt6JwC+U/PUSRbOM697VbPA18sBoVB7Hfu+cSgPRE135PVrmDNRm238oN+DWkRPtGhbM4y27dz/QH8nYirAbnZjiq8+8ypMvHWXODlZy7hWUtrfRuAqKb/A/U9SyJrF5Z9lmTQW4MeYIcMT++7SIvASsAG4ErrMvewB4GhXgTbnj6oGGS+S88Ec8OiHqzVwhHfxJe1YtXcz//PQHGVrVOALUIWm93LR+ZeBxXnGy2wUR1bbpeIV4T2a/ZWg+P3WjRFdOrh6vh0tvVUAkUGvPygPHK2CcYwkxpmkwjzfBlQAf8e0RJN3g83uTgCedAcERmo36jz++4avPvMr3X3krUd/w9wtonEIhazfDWDZwEVkNfAjYCyyzhTvGmCMicnHIdzYDmwEGBoq1YZcXeXubBOHVQJwQ9aqdUztKXgp/0p7NH728TkOK8rxx6sU/4fifJSgdwZ0fvyL2xloUDw//yexCNC8Gp86dXCXXXmEJQOezIFtv2sv3BfVoh+A7eV+mzkyH3tPxHPFGg/pXNEnywfu9SRyExhvJQe01OjHF+Oun6t57YfKka+bzJkaLk5d9aFW/G01cM8ZVevyKRN24ysDkElmAi8gvATuBu4wxv5CAMxCDMMZsA7YBDA8PtxIb0rVEsd+1otF77bVOgnyDZZOtVmx/7WqF1xtsbGWdtMdP0ITzyOgkGMNszYSmI0h78tywZuHJ7E60aLM28W8cDlywuGEZN6yZP3WqWUBOVOomhTmr5S2N2rheJWGTURQBnWRS9m9ow/wGaZjw9o8BJ6DnETuuwcvRX5ytyxHTLPNhWFnrAo/sPrhzbLIujiBJHvU4RBLgItKLJby/bozZZb99VESW29r3cuBYqiVTgGiHHKR1xl9PRfi1Dyzj6ZffcgXflk3r2PfGKR4ZOcxDPzzU0JSSZdIeP85S34nodFYJzt/tSkcwtCr8ZPYoGn7cjcMoATlRcTIuOqcXVSsLzTfNhHRUAR1FyfAGcVWAX155HutWnMe5i3rYd+QXC3L6e387yPPJn2YXcJUTL41WmI08X/wBQQYrLYSjlTuRvEnyqEcliheKAF8DXjLG/KXno0eBzwJfsf/fnWrJFKD5srnVZbX3+3M1w69edj5f/NjldZ32vqcOuPnEi7Dz7tiOnRWCiHUKU7VacTVwEWHw0vPaMqG0otWHbRyGeUA0C8hxiON14T29yLmP93utrlqiZqZ8pC6IS9jyyXXA/Iawk9PfX0b/GHA8n7zC2zEHBuFsvvpjCsKCuxyvmEW9lbpJAiwXRsfsWDOGJ186Sm9P+onpHKJo4NcCvwO8KCI/tt/7Mpbg/oaIfA44BNyaask6jKRmjmabK61GgoW5C3rLmHe0mR/vgK1iuO3DA1xqa9r73zztZjnc+ti+XP3so7Z51PqNGsQURWDWTdy+06erXpQAABMDSURBVIvSrq8oSoYzOTnU7L/93/3qM6/yvZ8dq8ti6a8Xr+dTtVrhlqGVDF56nuWGOFNDKoJgMIaFn8+GB64593Y8XZzTvn5w4LjrB/+J9y/jez875j6LMVZAWlYrwSheKM8SHvD1iVRL06G0YuaIsoRtJRIsqh0z7zwtXvwD1mu22HPwhJsHO8/VQiPtzV+HUevXe10jv+hGAtNxeTx++mzkg59b3WOJMkFtWLO03iRm5jXdilhGiGq14roVwnwWS+/mtBMRGpQm1uul4tSTd5UZFLjm7DlUKsITPz3qpuadtmMa7rrhvXVusl/82OVct/Ziy4xSM/T1Vhi89LzUTScOGonZBuKaOYKSCkXZFU9KlO8XyXOmkcArymrB3+a7xiZdl8ywAJ04njqNgpiC6sCba2bW2XCtCrddNdAw82UWp0OFKQmuL7ct+JxJaq5mEIEPrjyPkYn5WEFvFkvnNxuV1V/HkfqNPaHMzc3nVQdLow3aIwDLzr31RiuJVpJo4TioAG8DcYRK3ukpy0KYwCvKasHf5o7LWlr7CHG8Q4AFZz8CzM6Zpgc/p+W6GGWC8vtye90sjbHGhnOsnpN2IK39oKB+4+z9BFnOP/H+ZQv2CPz7CrcMraxzZ83VjVBJThyhUrRQ3TJShNVCkBDdNTaZ2sogjneIo637BVHULIHtXNF4zUALPI2a2JOdsk7P1BCR2C573joL8tAxWJOeYypxcFbMPz5c71/+4N5DdRGkubkRKq0TVagUxQSQhDxPJgkjzzL52zztlUGSPlWtWAcfX7RkUcODn731FiczX1yC/Le9K9DPf+Q93P/sz12zSiNzz9CqfrZsWue68SXdxPYG6FQqwuCK87jtw8GRnv6EXn687+XiRqi0l6KYAOJSRNNPkjJlnWskjzqJ26eC6i0owCXNGIQwz48l7+rl4S9eE7nsU2emqZnkLq8LzkSdM7wweYr9R/cF1oPXd93L/FHY8/Tk5EaotJm0B3o7tNAimn6SbB4XbRJKizh9Kmq9pRmD0ChlcZyyt7qCDQvQ8WdZdEw8Xt91L//+ygv551dPuD7h1g+lH4iuArzDaZdQKqLpJ26ZijgJ5UFSv/S4rohhwjott9j+xX3sORgtF7n3mZyTgUQsT5eax9XSn799NshuAvzzqyfYeuMgj48f4dlXjkcKvkqCCvAOp11CqYimn7hlynoSKuIeQRBJ/NIbXdfoEISg7zfSuKPUYZhLIQT74fu/6wTobBxcvsDu7XXfBFO3SemlVjOun/hzB08wO2eQSvxzPpuhArzDaadmXATvDz9xypTlJFQ280xcv/RGNFIi4rRPnDqMEsEZ9F0nTYMTuu+3e/vH0+9ds9raZLXTOtRqVoRnn3126v43T7t+97Nzhv1vnlYNXIlOXppxWbRNP1lNQmU3z0Rtz6imkiTEqUO/582TtvCG+QhOrz07LK+K/x5+Df2Oqwf4tXWXhEZ4bv32vrpyPfz8oVTz86gA7wLarRmXTdtsB0XcI4hKnMOZ45hK4hKnDr33fOPkO2z/4SH3MyeCM+zQj0Ype/0auuOm6BfyDsvOPQc45XudHpVUf01RCNaUuh1HoPzBf1hbugktans2um5oVT93fvyKptr7fU8dYHQi+GjduHXo3POm9Svp66lQEeipzEdwhpbX9hapGcPOscm68jhug2F14X+GL37scpzzRipCXQBQGqgGrqROmbXNLCniHkEUsvBK8RNVy09Sh2ErgKDyOlkRnajL7XsPsWPksHvEYH3K2+ZH3u1/83RdJKbawJXCU0SPFCU5aXulBJHWHkGYrT5M8HvPVAXc8PmZOeMe/uA/YhCsQJ1bhhYen+aG0tt29u/se7PufmoDV0pBWbVNJZg0vVKC8GvDYbnOGxFn78V/7TpPPvCeaoVf+8DFfO9nR5m1UxAuOGKwIghW2L2Ttvb0OzPzR7Vh5T3J2gauAlxRlNzxB+AkScHq1eK9hxVHiSJ1TvFxDri4aMkiROoD4nuqFe755DrG3zjlHoHnHOTgnLrjIFhh/detvZjv/PSo+/51awPPfk+MbmIqilIInE3HqTPTTTdNgzY8HS3eCa559pXjfOb+PYGbos61VcE9xcf72rGBe7llaCVrL1nC4bfPuEfgwXyovffyqu3p4k9epcmsFEXpaJpthjYyldy8fiXjr5/ihclTDU9lCrLX+0/s2TFqHcABUBU4c3aW2776XH1+E5tqVahgCf2Kx9Pluz4b+Ol3ZlKrJ1ABrihKwWi2Geo1f5ydqbFzbBKg7jCF3mrz4+KC/Le9wT33fHId3/rRJM+/NsWcgW/9+I3A3xHgtuHLuGn9yrpj3UYnpnjOt3rwv24VFeCKohSORpuh/Yv73EN6DZamDNTZsH/7qvmDrqPYzx2hffqdGe5/9ueW8K8Kc2ZhWliwbM89ntPmvbnVvauDX1lxXt33dBNTUZSuxYmE9Nqb5+ZqCIQedB3lN4MOZXBcCYMYXt3Plza+f8FhFPc+8XLd6uDY6bN131vcV433wE1QAa4oSttoJUeOIyDPztR7ezgC2zFhJPFBDzqUoSIgFWHOni28n1+5bP6kn51jk3z1mVd5ev8xpj0ziwFeO3Gm7jd/fPhk5HJFQQW4oihtIWzzMYpQ937XYJswqsKttu3Zm90wLv2L+xaenlMRNv3Kcr79whEMUK1YEfbGWPc1WD7g9zw6Xie0m/HBy86PXb5GqABXFKUthOUeiRJ84/1uReDaKy4M9fGOy9SZadf1UICPXHkhGweXs2X3uBt56bh4V8XSrB/64aGGBzqEceWyJS2X14v6gSuK0hb8vtdO7pEoibK83+3rqQQK72bJsKKUa1Gv9dvjb5wKFM7WJql15mbNGKoy/1lFFlwOWEK/KnBOr56JqShKSQlzD4ySAKuZa2ErKYz9vw3wyMjhwGurFUAsu3hF4Pr3LcMAFy9Z5GrmXrnf12NFbzrh9mmnl1ABrihK2wjyvY6aAKuRa2GrybC8v33fUwcWRGGCZV65/n3LePrlt5jFMFuDJ146Wndk266xSTeX+C1DK7nZTpKVVUplFeCKouRKGonP0kxhvGHNUnp7Km4UJljmkb6eChctWcSsJ+dJzViZB528K/7JaHRiitu3PcfMnOVXvn3zNZpOVlGU5OR93F0W908zhfHQqn62f2EDu8YmMcC5i3p47uAJlp17DusuPY++HuvU+hqWVl4DfnDgOHt//rardXtdDB0vlek564AIFeCKoiQi7+Pusrx/mimMvWH1t//tHlsbP8XT+49xz6cGmTozTf/iPh4fP8IPDhx3MyBu33uIXWOTbNlk2b0PHD1d97vHfYE9raICXFG6iLwPV877/nHZc/AEMx5TysycYerMtHtS/dpLlvD8a2+7gUDOYQ5bdo9TMwa/Y8pFSxalWj51I1SULiLIla+b7h8Xxx7u0FuVujI7pps7rh6grypUxTo0uWYcV8P631t3aX1ulFYRY+I5orfC8PCwGRkZadv9FEVZSNY2cOf3nax8/vvkbYOPy+jElGsP99q3/c/hfW7nQAqYF+IV4A//41pXe4+DiIwaY4b976sJRVG6jCyPu/MnhhKs4Bivrbtsx+0FlTfMlu+9bsvucTcYyPFi0UAeRVEKi9fGDTQ8VCFrouZYabRaCKOZLX/qzDQ1M38A8sAFi9n80cvbH8gjIn8HbAKOGWMG7fcuAB4GVgOvAZ82xsSLX1WULqNspoMkODZuN28J+di6o3i7ONc4G5COlhzFM6aZ37lbD7a74WsnzrDl0XHWXrKk7W6Efw/8b+AfPO/dDTxpjPmKiNxtv/5SaqVSlA4jb/e9duE/nDirEPJmRPF2ca5xT5KP4RnTzO/c+fxLO37Cgbf+FbCOW/vqM6+y7XcXmLIT01SAG2O+LyKrfW/fCFxn//0A8DQqwBUllLK5z7VCEWzcUSIz/VpyJaZnjPOMTph8kBB/96J6EXv0F/+W4GnCSWoDX2aMOQJgjDkiIheHXSgim4HNAAMDAwlvpyjlJs1Qb6U5USIzg1YL/Yv7QgUy1JvBoHkq3Ns+PMBPJl+se50mmW9iGmO2AdvAciPM+n6KUkTSDPVWohFlJeC9ppmZy//5TetXNl1V3XG1JbAfHz/CxsHl7uu0SCrAj4rIclv7Xg4cS7NQitKJFMG0oITTzMzl/9x/DmfYquqOqwdSF9wOSQX4o8Bnga/Y/+9OrUSKoigZE+QRFNWzxHtwcpRzOLP0PmoaiSki27E2LC8EjgJ/BnwL+AYwABwCbjXGvN3sZhqJqShK3jQylTQTtnGFcVrpZBNHYhpjbg/56BOxS6EoSkfRbt/2RveLWpZGppJGZi5v0E+jjU7vtT85fFLTySqKUjza7dveTHOOWpYkHkFRUgQE5ULxZyMMOTYzMSrAFUVJRLt92xvdL05ZkngENUsR4BXwFRHmasaK7gSqFaFWM67dPE1UgCuKkoh2+7Y3ul/cssT1CGqWIqBewBuqFcEYS2g7hzvksomZJrqJqSidRdFt4FHfi3PvoBQBjgbuTCBpC+2wTUwV4IqidCRBdnFoHj2ZlAf3HgoM2Al7Pw6aD1xRlK4iyC4OZGK3H52YYutj+zg7U+O5V6373HH1AA/uPcSXv2mF0v/TK8fd99NCj1RTFKUjCTq+Lasj3fYcPOGmpZ2tGbbsHmd0YoqHnz9Ud53/dauoBq4oSkcS5m2SRU6aDWuWUq2IewJPzRj2HDzBsnPPAU6511mv00MFuKIoqVG0QyuCvE2yyEkztKqfrTcOuqfR93k0/u/tP8bsnKGnKnzxY5enel8V4IqipEK3HFoRxh1XD7D2kiULJrCHN1+T2aSmAlxRlFTopkMrwmiXxu+gm5iKoqRCVhuEZWd0Yor7njrA6ET6xwarBq4oSirooRULydqspAJcUZTU0EMr6snarKQmFEVRlIzYsGYplYqVg1AqkrpZSQW4oihKRux/8zSzdj7w2TnD/jdPp/r7KsAVRVEy4vHxIw1ft4oKcEVRlIzYOLi84etW0U1MRVGUjHASV7WajTAMFeCKoigZcsfVA6kLbgc1oSiKopQUFeCKoiglRQW4oihKSVEBriiKUlJUgCuKopQUFeCKoiglpa2n0ovIW8BEwEcXAsfbVpBi0a3P3q3PDfrs3fjsrT73KmPMRf432yrAwxCREWPMcN7lyINuffZufW7QZ+/GZ8/qudWEoiiKUlJUgCuKopSUogjwbXkXIEe69dm79blBn70byeS5C2EDVxRFUeJTFA1cURRFiYkKcEVRlJKSqwAXkV8Xkf0ickBE7s6zLFkjIpeJyFMi8pKI7BOR37ffv0BEvisir9j/d+yJsCJSFZEfichj9uv3iMhe+9kfFpG+vMuYNiJyvojsEJGf2W1/Tbe0uYj8F7uvj4vIdhE5p1PbXET+TkSOici4573AdhaL/2XLvRdEZH3S++YmwEWkCtwHbAQ+ANwuIh/IqzxtYBb4Q2PM+4ENwJ32894NPGmMuRJ40n7dqfw+8JLn9V8Af2U/+xTwuVxKlS1/DfyjMeZ9wK9iPX/Ht7mIrAD+MzBsjBkEqsBv07lt/vfAr/veC2vnjcCV9r/NwN8kvWmeGvhVwAFjzEFjzDTwEHBjjuXJFGPMEWPMmP33aayBvALrmR+wL3sA+K18SpgtIrIS+E3gfvu1ANcDO+xLOu7ZReRc4KPA1wCMMdPGmJN0SZtjHRjzLhHpARYDR+jQNjfGfB942/d2WDvfCPyDsdgDnC8iic5ay1OArwAOe15P2u91PCKyGvgQsBdYZow5ApaQBy7Or2SZci/wR0DNfr0UOGmMmbVfd2L7rwHeAv6vbTq6X0TeTRe0uTHmdeB/AIewBPcpYJTOb3MvYe2cmuzLU4BLwHsd79MoIr8E7ATuMsb8Iu/ytAMR2QQcM8aMet8OuLTT2r8HWA/8jTHmQ8C/0oHmkiBse++NwHuAS4F3Y5kO/HRam0chtb6fpwCfBC7zvF4JvJFTWdqCiPRiCe+vG2N22W8fdZZP9v/H8ipfhlwLfEpEXsMylV2PpZGfby+voTPbfxKYNMbstV/vwBLo3dDmNwA/N8a8ZYyZAXYB/47Ob3MvYe2cmuzLU4A/D1xp70r3YW1wPJpjeTLFtvl+DXjJGPOXno8eBT5r//1ZYHe7y5Y1xpg/NsasNMasxmrn7xljPgM8BdxiX9Zxz26MeRM4LCJr7bc+AfyULmhzLNPJBhFZbPd959k7us19hLXzo8Dv2t4oG4BTjqklNsaY3P4BvwG8DLwK/EmeZWnDs34Ea5n0AvBj+99vYNmCnwResf+/IO+yZlwP1wGP2X+vAX4IHAAeARblXb4MnveDwIjd7t8C+rulzYH/BvwMGAf+H7CoU9sc2I5l65/B0rA/F9bOWCaU+2y59yKWp06i+2oovaIoSknRSExFUZSSogJcURSlpKgAVxRFKSkqwBVFUUqKCnBFUZSSogJcURSlpKgAVxRFKSn/H0og6q5ZiSXvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7yTu1D-PLEN",
        "colab_type": "code",
        "outputId": "29ed7608-1454-4cf5-8dcc-915681037fe2",
        "colab": {}
      },
      "source": [
        "fi=7\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goooFQh7PLEb",
        "colab_type": "code",
        "outputId": "5f88d8d1-4647-43b1-91fa-ba606d232513",
        "colab": {}
      },
      "source": [
        "fi=8\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwgSfQ0HPLEv",
        "colab_type": "code",
        "outputId": "5e3a126d-919d-411b-ddec-fdd349399fca",
        "colab": {}
      },
      "source": [
        "fi=9\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJay2hAAPLFM",
        "colab_type": "code",
        "outputId": "922ffe32-e191-488a-ac7e-8bf113a6c661",
        "colab": {}
      },
      "source": [
        "fi=10\n",
        "X=dataset['data'][:5,fi]\n",
        "y=dataset['target'][:5]\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TowOwo8fPLFb",
        "colab_type": "code",
        "outputId": "ea072cdc-dccf-4607-b3da-d07ca544e69e",
        "colab": {}
      },
      "source": [
        "fi=11\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXrikenaPLFq",
        "colab_type": "code",
        "outputId": "e73af8a8-f6fc-4fa6-b479-10294afc0e36",
        "colab": {}
      },
      "source": [
        "fi=12\n",
        "X=dataset['data'][:,fi]\n",
        "y=dataset['target']\n",
        "plt.plot(X,y,'.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3L2d5coPLF6",
        "colab_type": "code",
        "outputId": "f0f98f12-67cf-42ae-814e-700cdecb9a32",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X=dataset.data\n",
        "y=dataset.target\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y)\n",
        "print(X_train.shape,y_train.shape)\n",
        "print(X_test.shape,y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(379, 13) (379,)\n",
            "(127, 13) (127,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3MVGp_0PLGN",
        "colab_type": "code",
        "outputId": "d6350e1c-c00f-4107-fea0-c523bf153afd",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regression=LinearRegression()\n",
        "regression.fit(X_train,y_train)\n",
        "pred=regression.predict(X_test)\n",
        "regression.score(X_test,y_test)*100"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69.78383452608938"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ADcfPLlPLGc",
        "colab_type": "code",
        "outputId": "11998fd1-1ced-4e77-8add-7bfea76a69cf",
        "colab": {}
      },
      "source": [
        "print(regression.coef_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-9.39603031e-02  4.03546918e-02 -8.25507032e-04  2.52446312e+00\n",
            " -2.12546542e+01  4.22435759e+00  3.33181978e-03 -1.55778215e+00\n",
            "  2.59872145e-01 -1.06797522e-02 -1.03280793e+00  9.99557300e-03\n",
            " -4.52316898e-01]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2qmn3BwPLPb",
        "colab_type": "code",
        "outputId": "c98ae658-a21f-465f-9ec3-2856b68082cb",
        "colab": {}
      },
      "source": [
        "print(regression.intercept_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "36.307311078220955\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VZdmrIYPLRM",
        "colab_type": "code",
        "outputId": "8c2f2acc-3956-4070-e71b-c457962b794d",
        "colab": {}
      },
      "source": [
        "print(pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 8.99044189 37.49950005 25.76825732 30.22865661 19.76394116 19.96568049\n",
            " 26.04175801 12.0126767  20.18407567 23.49103543 22.3895452  19.37894872\n",
            " 24.28469217 14.31243523 23.7937724  16.78225655 20.02266309 14.73255904\n",
            " 32.92664217 30.96551883 24.85933997 24.49336243 24.75574626 21.83861086\n",
            " 20.15649843 17.81469294 33.57007477 18.22563892 20.69157874 20.96007193\n",
            " 24.69914209 32.07710021 13.04637193 23.85515724 26.7903596  13.42197177\n",
            " 32.73887825 15.50612905 17.54915407 12.06210535 24.3945939  22.83013969\n",
            " 25.88618422 19.76509577 25.50602318 33.62392848 19.09392196 40.56972735\n",
            " 35.55101561 16.6849922  12.81382381 25.56448284 19.79739538 12.47917739\n",
            " 14.66897523 25.66936756 25.16077271 19.89028614 28.35918867 30.06855098\n",
            " 27.71562424 21.46989929 22.40696759 26.56674796 11.9844373  18.14372248\n",
            " 35.65899385 27.05096079 33.7220326  28.56839753 31.5753378  32.45307601\n",
            " 27.03082337 29.99804821 24.45803023 15.38647203 21.41084074 19.71370157\n",
            " 30.83188224 33.08046433 42.33197737  9.96843199 13.01636128  6.67836696\n",
            " 32.84181799 14.2853475  20.26244833 14.30907099 25.1103207  23.78563917\n",
            " 24.38927776 28.85623828 24.42639347 20.28665453 16.58441393 43.43093599\n",
            " 27.13990829 18.55994534 19.15130096 13.2012761  13.69254681 20.03203434\n",
            " 22.84768454 33.34160036 24.50853218 27.10598994 21.46052895 11.36434114\n",
            " 37.69243493 17.10851193 22.55030851 17.87439257  8.8682674  21.86157716\n",
            " 35.83966915 29.31514612 25.45941916 31.91790883 21.03619539 17.87460168\n",
            " 13.36226344 20.36221909 17.11010763 23.93486871 21.5340405  35.76032607\n",
            " 17.03727198]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gr48jnWFPLRa",
        "colab_type": "code",
        "outputId": "72ef10d8-4cf6-4d28-d847-8a8c68e9c525",
        "colab": {}
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[11.8 44.  26.5 24.  20.5 15.  22.  27.5 24.1 19.4 21.1 22.2 22.8 21.9\n",
            " 26.2 19.4 19.9 15.4 31.5 24.8 25.  24.7 23.8 17.  13.1 20.  26.7 18.4\n",
            " 19.3 20.7 23.1 30.7 12.3 23.4 22.6 13.9 31.6 16.6 12.7 12.7 50.  24.7\n",
            " 23.9 18.4 15.  32.  19.5 46.  35.1 17.5 12.8 18.5 13.8  5.6  8.4 23.8\n",
            " 28.7 23.1 28.  23.5 22.  22.2 20.  23.3 12.7 22.6 46.7 22.8 50.  28.7\n",
            " 29.1 27.5 22.3 34.9 24.7 20.6 21.2 21.4 29.4 33.2 50.  17.8 13.6 10.2\n",
            " 33.1 13.5 22.9 14.  23.9 21.2 29.6 22.9 24.8 21.8 13.3 50.  23.9 17.5\n",
            " 18.  15.6 19.  20.3 21.7 30.3 19.1 24.5 18.8 10.8 43.1 17.1 17.8 19.5\n",
            "  8.7 18.9 33.4 24.6 25.  29.9 18.3 15.4 11.3 27.1 19.1 19.2 18.7 32.4\n",
            " 17.5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zoV7eWFPLSL",
        "colab_type": "code",
        "outputId": "d2a7c9dd-52d1-4aef-b6c3-4003c140c774",
        "colab": {}
      },
      "source": [
        "plt.plot(pred,y_test,'.')\n",
        "x_hyp=np.arange(5,45)\n",
        "y_hyp=np.arange(5,45)\n",
        "plt.plot(x_hyp,y_hyp)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEYo4ENjPLSb",
        "colab_type": "code",
        "outputId": "b2fdfd68-2ed0-44bd-e7b0-ee87203c8a74",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "np.round(mean_squared_error(y_test,pred),2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6p-yYo50PLTp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DO3SwEWhPLT8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}