Stock_Algorithms/Least_Angled_Regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Least-Angled Regression (LARS)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yfinance is used to fetch data\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-05-22T02:09:57.003Z",
          "iopub.execute_input": "2020-05-22T02:09:57.009Z",
          "iopub.status.idle": "2020-05-22T02:09:58.151Z",
          "shell.execute_reply": "2020-05-22T02:09:58.181Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "            Adj Close  Close  High   Low  Open    Volume\n",
              "Date                                                    \n",
              "2014-01-02       3.95   3.95  3.98  3.84  3.85  20548400\n",
              "2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200\n",
              "2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300\n",
              "2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100\n",
              "2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700"
            ],
            "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>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.85</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.98</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.01</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.19</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.23</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-05-22T02:09:58.162Z",
          "iopub.execute_input": "2020-05-22T02:09:58.169Z",
          "iopub.status.idle": "2020-05-22T02:09:59.468Z",
          "shell.execute_reply": "2020-05-22T02:09:59.589Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.reset_index()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "        Date  Adj Close  Close  High   Low  Open    Volume\n",
              "0 2014-01-02       3.95   3.95  3.98  3.84  3.85  20548400\n",
              "1 2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200\n",
              "2 2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300\n",
              "3 2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100\n",
              "4 2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700"
            ],
            "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>Date</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-01-02</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.85</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.98</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-01-06</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.01</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-01-07</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.19</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-01-08</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.23</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:09:59.479Z",
          "iopub.execute_input": "2020-05-22T02:09:59.484Z",
          "iopub.status.idle": "2020-05-22T02:09:59.497Z",
          "shell.execute_reply": "2020-05-22T02:09:59.594Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:09:59.505Z",
          "iopub.execute_input": "2020-05-22T02:09:59.512Z",
          "iopub.status.idle": "2020-05-22T02:09:59.520Z",
          "shell.execute_reply": "2020-05-22T02:10:00.230Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "           Date  Adj Close      Close   High        Low       Open     Volume  \\\n",
              "1166 2018-08-20  19.980000  19.980000  20.08  19.350000  19.790001   62983200   \n",
              "1167 2018-08-21  20.400000  20.400000  20.42  19.860001  19.980000   55629000   \n",
              "1168 2018-08-22  20.900000  20.900000  20.92  20.209999  20.280001   62002700   \n",
              "1169 2018-08-23  22.290001  22.290001  22.32  21.139999  21.190001  113444100   \n",
              "1170 2018-08-24  23.980000  23.980000  24.00  22.670000  22.910000  164328200   \n",
              "\n",
              "      Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "1166                  0                 1         1  0.010622  \n",
              "1167                  1                 1         1  0.021021  \n",
              "1168                  1                 1         1  0.024510  \n",
              "1169                  1                 1         1  0.066507  \n",
              "1170                  0                 0         0  0.075819  "
            ],
            "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>Date</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1166</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>19.980000</td>\n",
              "      <td>19.980000</td>\n",
              "      <td>20.08</td>\n",
              "      <td>19.350000</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>62983200</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.010622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1167</th>\n",
              "      <td>2018-08-21</td>\n",
              "      <td>20.400000</td>\n",
              "      <td>20.400000</td>\n",
              "      <td>20.42</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.980000</td>\n",
              "      <td>55629000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.021021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1168</th>\n",
              "      <td>2018-08-22</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>20.92</td>\n",
              "      <td>20.209999</td>\n",
              "      <td>20.280001</td>\n",
              "      <td>62002700</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.024510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1169</th>\n",
              "      <td>2018-08-23</td>\n",
              "      <td>22.290001</td>\n",
              "      <td>22.290001</td>\n",
              "      <td>22.32</td>\n",
              "      <td>21.139999</td>\n",
              "      <td>21.190001</td>\n",
              "      <td>113444100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.066507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1170</th>\n",
              "      <td>2018-08-24</td>\n",
              "      <td>23.980000</td>\n",
              "      <td>23.980000</td>\n",
              "      <td>24.00</td>\n",
              "      <td>22.670000</td>\n",
              "      <td>22.910000</td>\n",
              "      <td>164328200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.075819</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:09:59.528Z",
          "iopub.execute_input": "2020-05-22T02:09:59.534Z",
          "iopub.status.idle": "2020-05-22T02:09:59.545Z",
          "shell.execute_reply": "2020-05-22T02:10:00.234Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:09:59.552Z",
          "iopub.execute_input": "2020-05-22T02:09:59.556Z",
          "iopub.status.idle": "2020-05-22T02:10:00.148Z",
          "shell.execute_reply": "2020-05-22T02:10:00.236Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(dataset['Returns']).reshape(1, -1)\n",
        "y = np.array(dataset['Adj Close']).reshape(1, -1)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:00.159Z",
          "iopub.execute_input": "2020-05-22T02:10:00.167Z",
          "iopub.status.idle": "2020-05-22T02:10:00.176Z",
          "shell.execute_reply": "2020-05-22T02:10:00.239Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.Lars(n_nonzero_coefs=1)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:00.184Z",
          "iopub.execute_input": "2020-05-22T02:10:00.189Z",
          "iopub.status.idle": "2020-05-22T02:10:00.200Z",
          "shell.execute_reply": "2020-05-22T02:10:00.242Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.fit(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": [
              "Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,\n",
              "   fit_path=True, n_nonzero_coefs=1, normalize=True, positive=False,\n",
              "   precompute='auto', verbose=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:00.210Z",
          "iopub.execute_input": "2020-05-22T02:10:00.217Z",
          "iopub.status.idle": "2020-05-22T02:10:05.773Z",
          "shell.execute_reply": "2020-05-22T02:10:05.910Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(reg.coef_)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:05.782Z",
          "iopub.execute_input": "2020-05-22T02:10:05.788Z",
          "iopub.status.idle": "2020-05-22T02:10:05.800Z",
          "shell.execute_reply": "2020-05-22T02:10:05.914Z"
        }
      }
    },
    {
      "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 = 0.20, random_state = 0) "
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:05.808Z",
          "iopub.execute_input": "2020-05-22T02:10:05.814Z",
          "iopub.status.idle": "2020-05-22T02:10:05.820Z",
          "shell.execute_reply": "2020-05-22T02:10:05.916Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = reg.predict(X_test)"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:05.827Z",
          "iopub.execute_input": "2020-05-22T02:10:05.832Z",
          "iopub.status.idle": "2020-05-22T02:10:05.839Z",
          "shell.execute_reply": "2020-05-22T02:10:05.918Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:05.846Z",
          "iopub.execute_input": "2020-05-22T02:10:05.850Z",
          "iopub.status.idle": "2020-05-22T02:10:05.860Z",
          "shell.execute_reply": "2020-05-22T02:10:05.921Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_[0]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:05.867Z",
          "iopub.execute_input": "2020-05-22T02:10:05.870Z",
          "iopub.status.idle": "2020-05-22T02:10:05.879Z",
          "shell.execute_reply": "2020-05-22T02:10:05.923Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-22T02:10:05.885Z",
          "iopub.execute_input": "2020-05-22T02:10:05.889Z",
          "iopub.status.idle": "2020-05-22T02:10:05.898Z",
          "shell.execute_reply": "2020-05-22T02:10:05.925Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "file_extension": ".py",
      "version": "3.5.5",
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "name": "python",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.23.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}