yamanetoshi/machinelearning.ipynb Secret

## machinelearning.ipynb
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "machineLearning.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/yamanetoshi/b7b16c8385bc0960d52663044c2c02be/machinelearning.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "Ypy6py9gOqTa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "a3795460-5865-45e2-b300-c89b4faeaabd"
      },
      "cell_type": "code",
      "source": [
        "!pip install numpy scipy pandas matplotlib scikit-learn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.14.5)\r\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (0.19.1)\r\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (0.22.0)\r\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (2.1.2)\r\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (0.19.1)\r\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.5.3)\r\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.5)\r\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.11.0)\r\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.2.0)\r\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MfgNpLnVQShI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "bf529d77-168a-4fe8-e176-e52fc319b9da"
      },
      "cell_type": "code",
      "source": [
        "%lsmagic"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/json": {
              "cell": {
                "prun": "ExecutionMagics",
                "file": "Other",
                "!": "OSMagics",
                "capture": "ExecutionMagics",
                "timeit": "ExecutionMagics",
                "script": "ScriptMagics",
                "pypy": "Other",
                "system": "OSMagics",
                "perl": "Other",
                "html": "DisplayMagics",
                "bash": "Other",
                "HTML": "Other",
                "python": "Other",
                "SVG": "Other",
                "javascript": "DisplayMagics",
                "bigquery": "Other",
                "js": "DisplayMagics",
                "writefile": "OSMagics",
                "ruby": "Other",
                "python3": "Other",
                "python2": "Other",
                "latex": "DisplayMagics",
                "sx": "OSMagics",
                "svg": "DisplayMagics",
                "sh": "Other",
                "time": "ExecutionMagics",
                "debug": "ExecutionMagics"
              },
              "line": {
                "psource": "NamespaceMagics",
                "logstart": "LoggingMagics",
                "popd": "OSMagics",
                "loadpy": "CodeMagics",
                "colors": "BasicMagics",
                "who_ls": "NamespaceMagics",
                "lf": "Other",
                "ll": "Other",
                "pprint": "BasicMagics",
                "lk": "Other",
                "ls": "Other",
                "save": "CodeMagics",
                "tb": "ExecutionMagics",
                "lx": "Other",
                "pylab": "PylabMagics",
                "killbgscripts": "ScriptMagics",
                "quickref": "BasicMagics",
                "magic": "BasicMagics",
                "dhist": "OSMagics",
                "edit": "KernelMagics",
                "logstop": "LoggingMagics",
                "gui": "BasicMagics",
                "prun": "ExecutionMagics",
                "debug": "ExecutionMagics",
                "page": "BasicMagics",
                "logstate": "LoggingMagics",
                "ed": "Other",
                "pushd": "OSMagics",
                "timeit": "ExecutionMagics",
                "set_env": "OSMagics",
                "rehashx": "OSMagics",
                "hist": "Other",
                "qtconsole": "KernelMagics",
                "rm": "Other",
                "dirs": "OSMagics",
                "run": "ExecutionMagics",
                "reset_selective": "NamespaceMagics",
                "pinfo2": "NamespaceMagics",
                "matplotlib": "PylabMagics",
                "unload_ext": "ExtensionMagics",
                "doctest_mode": "BasicMagics",
                "logoff": "LoggingMagics",
                "reload_ext": "ExtensionMagics",
                "pdb": "ExecutionMagics",
                "load": "CodeMagics",
                "lsmagic": "BasicMagics",
                "autosave": "KernelMagics",
                "cd": "OSMagics",
                "pastebin": "CodeMagics",
                "alias_magic": "BasicMagics",
                "cp": "Other",
                "autocall": "AutoMagics",
                "ldir": "Other",
                "bookmark": "OSMagics",
                "connect_info": "KernelMagics",
                "mkdir": "Other",
                "system": "OSMagics",
                "whos": "NamespaceMagics",
                "rmdir": "Other",
                "automagic": "AutoMagics",
                "store": "StoreMagics",
                "more": "KernelMagics",
                "pdef": "NamespaceMagics",
                "precision": "BasicMagics",
                "pinfo": "NamespaceMagics",
                "pwd": "OSMagics",
                "psearch": "NamespaceMagics",
                "reset": "NamespaceMagics",
                "recall": "HistoryMagics",
                "xdel": "NamespaceMagics",
                "xmode": "BasicMagics",
                "cat": "Other",
                "mv": "Other",
                "rerun": "HistoryMagics",
                "logon": "LoggingMagics",
                "history": "HistoryMagics",
                "pycat": "OSMagics",
                "unalias": "OSMagics",
                "env": "OSMagics",
                "load_ext": "ExtensionMagics",
                "config": "ConfigMagics",
                "profile": "BasicMagics",
                "pfile": "NamespaceMagics",
                "less": "KernelMagics",
                "who": "NamespaceMagics",
                "notebook": "BasicMagics",
                "man": "KernelMagics",
                "sx": "OSMagics",
                "macro": "ExecutionMagics",
                "clear": "KernelMagics",
                "alias": "OSMagics",
                "time": "ExecutionMagics",
                "sc": "OSMagics",
                "rep": "Other",
                "pdoc": "NamespaceMagics"
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            },
            "text/plain": [
              "Available line magics:\n",
              "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
              "\n",
              "Available cell magics:\n",
              "%%!  %%HTML  %%SVG  %%bash  %%bigquery  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
              "\n",
              "Automagic is ON, % prefix IS NOT needed for line magics."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "0V4Zu1DBRcKK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d41f4ce3-303e-4ed6-8196-a99484490cfa"
      },
      "cell_type": "code",
      "source": [
        "from random import randint\n",
        "\n",
        "%timeit -n 10 -r 3 [randint(0, 100) for i in range(10)]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10 loops, best of 3: 13.5 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dR3KXTSSRrWt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9ad69421-bda6-46b6-f43b-56f8caafeb81"
      },
      "cell_type": "code",
      "source": [
        "%%timeit -n 10 -r 3\n",
        "\n",
        "for i in range(10):\n",
        "  randint(0, 100)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10 loops, best of 3: 13.2 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ePpskeCuZ9dU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "ddd652b4-1e75-4980-8994-f52a566e82db"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "y = np.array([7, 8, 9])\n",
        "\n",
        "print (x)\n",
        "print (y)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "[7 8 9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kh-tkafhaRmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "55f0b340-ea77-4ad8-9ca6-358719908d10"
      },
      "cell_type": "code",
      "source": [
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2, 3)\n",
            "(3,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2Ced5-e5aXVL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "755a5022-b35c-472b-eb50-86110e2edad6"
      },
      "cell_type": "code",
      "source": [
        "a = np.array([7, 8, 9])\n",
        "a + 1"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8,  9, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "P814VP7YbXcT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "41a89838-c3ab-442b-df3f-e4d9655ffca6"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "d = {'employee_id': [101, 102, 103, 104], 'name': [\"Tom\", \"Brian\", \"Lisa\", \"Ray\"]}\n",
        "df = pd.DataFrame(d)\n",
        "df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>employee_id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>Tom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>Brian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>Lisa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>Ray</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   employee_id   name\n",
              "0          101    Tom\n",
              "1          102  Brian\n",
              "2          103   Lisa\n",
              "3          104    Ray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "sbJmf2Pdd1_u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sqQJ999Id8OW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a8a7c74b-b2fc-472e-eae4-2c466fbad9e1"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "x = [1, 2, 3, 4]\n",
        "y = [10, 12, 15, 20]\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fZ4CdtppNpZUcPFkPQEpiLMsWTKC4KIeJY1JdTigi0n8RX97HzzSxqayC7Xur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\nIiIepPIWEfEglbeIiAf9f173/oTKkKQiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8c89da26d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gBpEtN55eb6u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "a4e81623-85e6-4613-ee2f-2a18ae3cd420"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plot\n",
        "from numpy.random import normal\n",
        "\n",
        "x = normal(size=200)\n",
        "plt.hist(x, bins=40)\n",
        "\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y (Bin Count)')\n",
        "plt.title('Histgram Example')\n",
        "plt.axis([-4, 4, 0, 25])\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8c89d458d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ynG5KRy5fDCl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "c12fa259-e26c-4738-e766-98a6fa4681db"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = np.arange(20)\n",
        "y = 2*x + 1\n",
        "\n",
        "m, b = np.polyfit(x, y, deg=1)\n",
        "plt.plot(x, m*x + b, 'r-')\n",
        "\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.title('Linear Regression Example')\n",
        "plt.axis([0, 21, 0, 42])\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dT5kwjchtwMfOubFNV6zndeC7Jm9CD9NoTDol6P47DFjnsI5z7kPg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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8c87c48710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8PxmCdT0gULX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6f6e16f2-b6f2-410a-ba31-e473a11f17b9"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.metrics import r2_score\n",
        "diabetes = datasets.load_diabetes()\n",
        "\n",
        "df = pd.DataFrame(diabetes.data, columns=(\"age\", \"sex\", \"bmi\", \"map\", \"tc\", \"ldl\", \"hdl\", \"tch\", \"ltg\", \"glu\"))\n",
        "df['target'] = diabetes.target\n",
        "df.head()\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>map</th>\n",
              "      <th>tc</th>\n",
              "      <th>ldl</th>\n",
              "      <th>hdl</th>\n",
              "      <th>tch</th>\n",
              "      <th>ltg</th>\n",
              "      <th>glu</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.038076</td>\n",
              "      <td>0.050680</td>\n",
              "      <td>0.061696</td>\n",
              "      <td>0.021872</td>\n",
              "      <td>-0.044223</td>\n",
              "      <td>-0.034821</td>\n",
              "      <td>-0.043401</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>0.019908</td>\n",
              "      <td>-0.017646</td>\n",
              "      <td>151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.001882</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.051474</td>\n",
              "      <td>-0.026328</td>\n",
              "      <td>-0.008449</td>\n",
              "      <td>-0.019163</td>\n",
              "      <td>0.074412</td>\n",
              "      <td>-0.039493</td>\n",
              "      <td>-0.068330</td>\n",
              "      <td>-0.092204</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.085299</td>\n",
              "      <td>0.050680</td>\n",
              "      <td>0.044451</td>\n",
              "      <td>-0.005671</td>\n",
              "      <td>-0.045599</td>\n",
              "      <td>-0.034194</td>\n",
              "      <td>-0.032356</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>0.002864</td>\n",
              "      <td>-0.025930</td>\n",
              "      <td>141.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.089063</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.011595</td>\n",
              "      <td>-0.036656</td>\n",
              "      <td>0.012191</td>\n",
              "      <td>0.024991</td>\n",
              "      <td>-0.036038</td>\n",
              "      <td>0.034309</td>\n",
              "      <td>0.022692</td>\n",
              "      <td>-0.009362</td>\n",
              "      <td>206.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.005383</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.036385</td>\n",
              "      <td>0.021872</td>\n",
              "      <td>0.003935</td>\n",
              "      <td>0.015596</td>\n",
              "      <td>0.008142</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>-0.031991</td>\n",
              "      <td>-0.046641</td>\n",
              "      <td>135.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        age       sex       bmi       map        tc       ldl       hdl  \\\n",
              "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
              "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
              "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
              "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
              "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
              "\n",
              "        tch       ltg       glu  target  \n",
              "0 -0.002592  0.019908 -0.017646   151.0  \n",
              "1 -0.039493 -0.068330 -0.092204    75.0  \n",
              "2 -0.002592  0.002864 -0.025930   141.0  \n",
              "3  0.034309  0.022692 -0.009362   206.0  \n",
              "4 -0.002592 -0.031991 -0.046641   135.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "nd93YvVvhuZd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1f10a2b7-6077-4e8e-99f3-3e327ec1e9e0"
      },
      "cell_type": "code",
      "source": [
        "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
        "\n",
        "diabetes_X_train = diabetes_X[:-20]\n",
        "diabetes_X_test = diabetes_X[-20:]\n",
        "\n",
        "diabetes_Y_train = diabetes.target[:-20]\n",
        "diabetes_Y_test = diabetes.target[-20:]\n",
        "\n",
        "regr = linear_model.LinearRegression()\n",
        "\n",
        "regr.fit(diabetes_X_train, diabetes_Y_train)\n",
        "\n",
        "diabetes_y_pred = regr.predict(diabetes_X_test)\n",
        "\n",
        "print('r^2 score: %.2f' % r2_score(diabetes_Y_test, diabetes_y_pred))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r^2 score: 0.47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jnGr8Bjui7KO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "bf50bc1a-0baa-4c8f-d03c-d3dee7969374"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(diabetes_X_test, diabetes_Y_test, color='black')\n",
        "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
        "\n",
        "plt.xticks(())\n",
        "plt.yticks(())\n",
        "\n",
        "plt.xlabel('bmi')\n",
        "plt.ylabel('target')\n",
        "plt.title('Linear Regression between bmi and target')\n",
        "plt.grid(True)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8c87caba58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}