Features_Scores.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Features Scores"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import math\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "C:\\Users\\Tin Hang\\Anaconda3\\envs\\py35\\lib\\site-packages\\sklearn\\utils\\fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
            "  _nan_object_mask = _nan_object_array != _nan_object_array\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_name = 'AMD'\n",
        "start = '2018-01-01' \n",
        "end = '2018-09-14'\n",
        "df = yf.download(stock_name, start, end)\n",
        "df = df.reset_index()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>10.42</td>\n",
              "      <td>11.02</td>\n",
              "      <td>10.34</td>\n",
              "      <td>10.98</td>\n",
              "      <td>10.98</td>\n",
              "      <td>44146300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>11.61</td>\n",
              "      <td>12.14</td>\n",
              "      <td>11.36</td>\n",
              "      <td>11.55</td>\n",
              "      <td>11.55</td>\n",
              "      <td>154066700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>12.10</td>\n",
              "      <td>12.43</td>\n",
              "      <td>11.97</td>\n",
              "      <td>12.12</td>\n",
              "      <td>12.12</td>\n",
              "      <td>109503000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>12.19</td>\n",
              "      <td>12.22</td>\n",
              "      <td>11.66</td>\n",
              "      <td>11.88</td>\n",
              "      <td>11.88</td>\n",
              "      <td>63808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-08</td>\n",
              "      <td>12.01</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.85</td>\n",
              "      <td>12.28</td>\n",
              "      <td>12.28</td>\n",
              "      <td>63346000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date   Open   High    Low  Close  Adj Close     Volume\n",
              "0 2018-01-02  10.42  11.02  10.34  10.98      10.98   44146300\n",
              "1 2018-01-03  11.61  12.14  11.36  11.55      11.55  154066700\n",
              "2 2018-01-04  12.10  12.43  11.97  12.12      12.12  109503000\n",
              "3 2018-01-05  12.19  12.22  11.66  11.88      11.88   63808900\n",
              "4 2018-01-08  12.01  12.30  11.85  12.28      12.28   63346000"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
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        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = ['Adj_Close' if x=='Adj Close' else x for x in df.columns]\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj_Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>10.42</td>\n",
              "      <td>11.02</td>\n",
              "      <td>10.34</td>\n",
              "      <td>10.98</td>\n",
              "      <td>10.98</td>\n",
              "      <td>44146300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>11.61</td>\n",
              "      <td>12.14</td>\n",
              "      <td>11.36</td>\n",
              "      <td>11.55</td>\n",
              "      <td>11.55</td>\n",
              "      <td>154066700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>12.10</td>\n",
              "      <td>12.43</td>\n",
              "      <td>11.97</td>\n",
              "      <td>12.12</td>\n",
              "      <td>12.12</td>\n",
              "      <td>109503000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>12.19</td>\n",
              "      <td>12.22</td>\n",
              "      <td>11.66</td>\n",
              "      <td>11.88</td>\n",
              "      <td>11.88</td>\n",
              "      <td>63808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-08</td>\n",
              "      <td>12.01</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.85</td>\n",
              "      <td>12.28</td>\n",
              "      <td>12.28</td>\n",
              "      <td>63346000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date   Open   High    Low  Close  Adj_Close     Volume\n",
              "0 2018-01-02  10.42  11.02  10.34  10.98      10.98   44146300\n",
              "1 2018-01-03  11.61  12.14  11.36  11.55      11.55  154066700\n",
              "2 2018-01-04  12.10  12.43  11.97  12.12      12.12  109503000\n",
              "3 2018-01-05  12.19  12.22  11.66  11.88      11.88   63808900\n",
              "4 2018-01-08  12.01  12.30  11.85  12.28      12.28   63346000"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Single Linear Regression"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Use one feature\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols\n",
        "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
        "\n\nstock_model = ols(\"Adj_Close ~ Open\", data=df).fit()"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_model.summary()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>        <td>Adj_Close</td>    <th>  R-squared:         </th> <td>   0.989</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.989</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.616e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Sat, 26 Jan 2019</td> <th>  Prob (F-statistic):</th> <td>4.22e-175</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>21:20:26</td>     <th>  Log-Likelihood:    </th> <td> -134.06</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   178</td>      <th>  AIC:               </th> <td>   272.1</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   176</td>      <th>  BIC:               </th> <td>   278.5</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>   -0.1905</td> <td>    0.124</td> <td>   -1.532</td> <td> 0.127</td> <td>   -0.436</td> <td>    0.055</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Open</th>      <td>    1.0168</td> <td>    0.008</td> <td>  127.129</td> <td> 0.000</td> <td>    1.001</td> <td>    1.033</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>44.008</td> <th>  Durbin-Watson:     </th> <td>   2.561</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 561.550</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.361</td> <th>  Prob(JB):          </th> <td>1.15e-122</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td>11.671</td> <th>  Cond. No.          </th> <td>    50.1</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:              Adj_Close   R-squared:                       0.989\n",
              "Model:                            OLS   Adj. R-squared:                  0.989\n",
              "Method:                 Least Squares   F-statistic:                 1.616e+04\n",
              "Date:                Sat, 26 Jan 2019   Prob (F-statistic):          4.22e-175\n",
              "Time:                        21:20:26   Log-Likelihood:                -134.06\n",
              "No. Observations:                 178   AIC:                             272.1\n",
              "Df Residuals:                     176   BIC:                             278.5\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept     -0.1905      0.124     -1.532      0.127      -0.436       0.055\n",
              "Open           1.0168      0.008    127.129      0.000       1.001       1.033\n",
              "==============================================================================\n",
              "Omnibus:                       44.008   Durbin-Watson:                   2.561\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              561.550\n",
              "Skew:                          -0.361   Prob(JB):                    1.15e-122\n",
              "Kurtosis:                      11.671   Cond. No.                         50.1\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "\nfig = sm.graphics.plot_regress_exog(stock_model, \"Open\", fig=fig)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fabfe5c0>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# predictor variable (X) and dependent variable (y)\n",
        "X = df[['Open']]\n",
        "y = df[['Adj_Close']]"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "plt.plot(X, y, 'ro')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fabfe400>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Stock Charts\n",
        "_, confidence_interval_lower, confidence_interval_upper = wls_prediction_std(stock_model)\n",
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "\n",
        "ax.plot(X, y, 'bo', label=\"data\")\n",
        "\n",
        "# Plot trend line\n",
        "ax.plot(X, stock_model.fittedvalues, 'g--.', label=\"OLS\")\n",
        "\n",
        "# Plot upper and lower confidence interval\n",
        "ax.plot(X, confidence_interval_upper, 'r--')\n",
        "ax.plot(X, confidence_interval_lower, 'r--')\n",
        "\n",
        "# Plot title, grid, and legend\n",
        "ax.set_title('Simple Linear Regression')\n",
        "ax.grid()\n",
        "ax.legend(loc='best')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x280fb556320>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fb7d1358>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        " a = model.coef_ * X + model.intercept_\n",
        " a"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "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>Open</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.404165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.614108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.112319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.203827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.020811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12.010643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>11.634443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>12.081816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11.827627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12.061481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11.908967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12.193660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12.580028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12.630866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>12.844385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>13.047737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>12.803715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>12.722374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>13.159580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>13.149413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>13.271424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>13.657792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>13.078240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>12.061481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>11.034555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>11.705616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>11.725951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>11.542934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>11.471761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>11.603940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>19.066948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>19.016108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>19.666835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>19.595660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>19.717672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>19.219461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>19.290634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>20.114207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>20.002366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>20.002366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>19.249964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>19.931193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>20.124376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>20.429404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>21.354654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>23.103478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>25.167499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>25.747050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>24.577779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>25.523364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>25.116659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>25.858894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>29.712408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>28.400790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>27.221349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>28.431292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>30.332630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>30.220787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>33.525252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>31.766260</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Open\n",
              "0    10.404165\n",
              "1    11.614108\n",
              "2    12.112319\n",
              "3    12.203827\n",
              "4    12.020811\n",
              "5    12.010643\n",
              "6    11.634443\n",
              "7    12.081816\n",
              "8    11.827627\n",
              "9    12.061481\n",
              "10   11.908967\n",
              "11   12.193660\n",
              "12   12.580028\n",
              "13   12.630866\n",
              "14   12.844385\n",
              "15   13.047737\n",
              "16   12.803715\n",
              "17   12.722374\n",
              "18   13.159580\n",
              "19   13.149413\n",
              "20   13.271424\n",
              "21   13.657792\n",
              "22   13.078240\n",
              "23   12.061481\n",
              "24   11.034555\n",
              "25   11.705616\n",
              "26   11.725951\n",
              "27   11.542934\n",
              "28   11.471761\n",
              "29   11.603940\n",
              "..         ...\n",
              "148  19.066948\n",
              "149  19.016108\n",
              "150  19.666835\n",
              "151  19.595660\n",
              "152  19.717672\n",
              "153  19.219461\n",
              "154  19.290634\n",
              "155  20.114207\n",
              "156  20.002366\n",
              "157  20.002366\n",
              "158  19.249964\n",
              "159  19.931193\n",
              "160  20.124376\n",
              "161  20.429404\n",
              "162  21.354654\n",
              "163  23.103478\n",
              "164  25.167499\n",
              "165  25.747050\n",
              "166  24.577779\n",
              "167  25.523364\n",
              "168  25.116659\n",
              "169  25.858894\n",
              "170  29.712408\n",
              "171  28.400790\n",
              "172  27.221349\n",
              "173  28.431292\n",
              "174  30.332630\n",
              "175  30.220787\n",
              "176  33.525252\n",
              "177  31.766260\n",
              "\n[178 rows x 1 columns]"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(X, y, 'ro', X, a)\n",
        "axes = plt.gca()\n",
        "# axes.set_ylim([0, 30])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fb511780>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multiple Linear Regression"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Multi Features\n",
        "stock_models = ols(\"Adj_Close ~ Open + High + Low + Volume\", data=df).fit()"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_models.summary()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>        <td>Adj_Close</td>    <th>  R-squared:         </th> <td>   0.998</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.998</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>2.171e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Sat, 26 Jan 2019</td> <th>  Prob (F-statistic):</th> <td>1.82e-232</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>21:20:28</td>     <th>  Log-Likelihood:    </th> <td>  16.329</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   178</td>      <th>  AIC:               </th> <td>  -22.66</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   173</td>      <th>  BIC:               </th> <td>  -6.750</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>   -0.0308</td> <td>    0.066</td> <td>   -0.466</td> <td> 0.642</td> <td>   -0.161</td> <td>    0.100</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Open</th>      <td>   -0.6663</td> <td>    0.062</td> <td>  -10.799</td> <td> 0.000</td> <td>   -0.788</td> <td>   -0.544</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>High</th>      <td>    1.0066</td> <td>    0.089</td> <td>   11.350</td> <td> 0.000</td> <td>    0.832</td> <td>    1.182</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Low</th>       <td>    0.6671</td> <td>    0.079</td> <td>    8.408</td> <td> 0.000</td> <td>    0.511</td> <td>    0.824</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Volume</th>    <td> -2.43e-09</td> <td>  7.9e-10</td> <td>   -3.075</td> <td> 0.002</td> <td>-3.99e-09</td> <td> -8.7e-10</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>59.860</td> <th>  Durbin-Watson:     </th> <td>   2.285</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 352.974</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-1.087</td> <th>  Prob(JB):          </th> <td>2.25e-77</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 9.547</td> <th>  Cond. No.          </th> <td>6.30e+08</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.3e+08. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:              Adj_Close   R-squared:                       0.998\n",
              "Model:                            OLS   Adj. R-squared:                  0.998\n",
              "Method:                 Least Squares   F-statistic:                 2.171e+04\n",
              "Date:                Sat, 26 Jan 2019   Prob (F-statistic):          1.82e-232\n",
              "Time:                        21:20:28   Log-Likelihood:                 16.329\n",
              "No. Observations:                 178   AIC:                            -22.66\n",
              "Df Residuals:                     173   BIC:                            -6.750\n",
              "Df Model:                           4                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept     -0.0308      0.066     -0.466      0.642      -0.161       0.100\n",
              "Open          -0.6663      0.062    -10.799      0.000      -0.788      -0.544\n",
              "High           1.0066      0.089     11.350      0.000       0.832       1.182\n",
              "Low            0.6671      0.079      8.408      0.000       0.511       0.824\n",
              "Volume      -2.43e-09    7.9e-10     -3.075      0.002   -3.99e-09    -8.7e-10\n",
              "==============================================================================\n",
              "Omnibus:                       59.860   Durbin-Watson:                   2.285\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              352.974\n",
              "Skew:                          -1.087   Prob(JB):                     2.25e-77\n",
              "Kurtosis:                       9.547   Cond. No.                     6.30e+08\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 6.3e+08. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(20,12))\n",
        "fig = sm.graphics.plot_partregress_grid(stock_models, fig=fig)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fb704c88>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2nd order polynomial\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "X = df[['High', 'Low']]\n",
        "y = df[['Adj_Close']]\n",
        "poly_2 = PolynomialFeatures(degree=2)\n",
        "X = poly_2.fit_transform(X)"
      ],
      "outputs": [],
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = LinearRegression()\n",
        "model2.fit(X, y)\n",
        "model2.coef_"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": [
              "array([[ 0.        , -0.0642992 ,  1.08980114, -0.51337035,  1.12375418,\n",
              "        -0.61322593]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": [
              "0.99753836829697173"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2nd order polynomial\n",
        "X = df[['Open', 'Low']]\n",
        "y = df[['Adj_Close']]\n",
        "poly_2 = PolynomialFeatures(degree=2)\n",
        "X = poly_2.fit_transform(X)\n",
        "\n",
        "model2 = LinearRegression()\n",
        "model2.fit(X, y)\n",
        "model2.coef_\n",
        "\nmodel2.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": [
              "0.99622255253000191"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2nd order polynomial\n",
        "X = df[['Open', 'High']]\n",
        "y = df[['Adj_Close']]\n",
        "poly_2 = PolynomialFeatures(degree=2)\n",
        "X = poly_2.fit_transform(X)\n",
        "\n",
        "model2 = LinearRegression()\n",
        "model2.fit(X, y)\n",
        "model2.coef_\n",
        "\nmodel2.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": [
              "0.9958532214170388"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "X = df[['Open', 'High']].values\n",
        "y = df[['Adj_Close']].values"
      ],
      "outputs": [],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(X)\n",
        "y = np.array(y)"
      ],
      "outputs": [],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "poly = PolynomialFeatures(degree=2)\n",
        "poly_features = poly.fit_transform(X)\n",
        "poly.fit(X,y)\n",
        "poly_regression = LinearRegression()\n",
        "poly_regression.fit(poly_features,y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(178, 2)\n",
            "(178, 1)\n"
          ]
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X[:,:-1]\n",
        "X.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": [
              "(178, 1)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Slicing with [:, :-1] will give you a 2-dimensional array (including all rows and all columns excluding the last column).\n",
        "\n",
        "# Slicing with [:, 1] will give you a 1-dimensional array (including all rows from the second column). \n",
        "# To make this array also 2-dimensional use [:, 1:2] or [:, 1].reshape(-1, 1) or [:, 1][:, None] instead of [:, 1]. This will make x and y comparable.\n",
        "\n",
        "# An alternative to making both arrays 2-dimensional is making them both one dimensional.\n",
        "# For this one would do [:, 0] (instead of [:, :1]) for selecting the first column and [:, 1] for selecting the second column."
      ],
      "outputs": [],
      "execution_count": 28,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the data for Plynomial Regression \n",
        "regressor=LinearRegression()\n",
        "regressor.fit(X,y)\n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X,poly_regression.predict(poly_features))\n",
        "plt.title(\"Polynomial Regression with degree 2\")\n",
        "plt.xlabel(\"Stock Features\")\n",
        "plt.ylabel(\"Stock Prices\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fbc94390>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 29,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the Linear Regression\n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X,regressor.predict(X))\n",
        "plt.title(\"Linear Regression\")\n",
        "plt.xlabel(\"Stock Features\")\n",
        "plt.ylabel(\"Stock Prices\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fb4ea3c8>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 30,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3rd order polynomial\n",
        "X = df[['Open', 'High', 'Low']]\n",
        "y = df[['Adj_Close']]\n",
        "poly_3 = PolynomialFeatures(degree=3)\n",
        "X = poly_3.fit_transform(X)"
      ],
      "outputs": [],
      "execution_count": 31,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = LinearRegression()\n",
        "model3.fit(X, y)\n",
        "model3.coef_"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": [
              "array([[  0.00000000e+00,   5.20014004e-02,   7.05613812e-01,\n",
              "          2.05588478e-01,   1.18715008e+00,   8.52325294e-01,\n",
              "         -3.45073305e+00,  -1.64394354e+00,   2.61627771e+00,\n",
              "          4.40527917e-01,   4.59426084e-03,   1.94696861e+00,\n",
              "         -2.17649113e+00,   2.94783472e-01,  -4.68297000e+00,\n",
              "          4.85254693e+00,  -1.68339320e+00,   5.12542737e+00,\n",
              "         -3.07618731e+00,  -6.05134136e-01]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 32,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model3.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": [
              "0.99899609260978406"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 33,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Open', 'High', 'Low']].values\n",
        "y = df[['Adj_Close']].values\n",
        "poly = PolynomialFeatures(degree=3)\n",
        "poly_features = poly.fit_transform(X)\n",
        "poly.fit(X,y)\n",
        "poly_regression = LinearRegression()\n",
        "poly_regression.fit(poly_features,y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X[:,:-2]\n",
        "X.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": [
              "(178, 1)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the data for Plynomial Regression \n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X,poly_regression.predict(poly_features))\n",
        "plt.title(\"Polynomial Regression with degree 3\")\n",
        "plt.xlabel(\"Stock Features\")\n",
        "plt.ylabel(\"Stock Prices\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fb7f2d68>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 36,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the Linear Regression\n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X,regressor.predict(X))\n",
        "plt.title(\"Linear Regression\")\n",
        "plt.xlabel(\"Stock Features\")\n",
        "plt.ylabel(\"Stock Prices\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fad9d470>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4th order polynomial\n",
        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
        "y = df[['Adj_Close']]\n",
        "poly_4 = PolynomialFeatures(degree=4)\n",
        "X = poly_4.fit_transform(X)"
      ],
      "outputs": [],
      "execution_count": 38,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model4 = LinearRegression()\n",
        "model4.fit(X, y)\n",
        "model4.coef_"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": [
              "array([[ -5.48458520e-21,  -7.60211085e-20,   2.07537326e-24,\n",
              "         -2.04480116e-25,  -4.91043594e-26,  -5.60092375e-25,\n",
              "         -1.56522787e-20,  -5.75171025e-19,  -1.20939904e-19,\n",
              "         -1.35785275e-19,  -5.82729648e-19,   1.53265888e-18,\n",
              "          1.46552795e-33,   4.85038222e-27,   1.02509567e-20,\n",
              "          2.78191249e-32,   2.72658707e-32,   2.71659247e-32,\n",
              "          8.23166523e-26,   2.67197924e-32,   2.66662114e-32,\n",
              "          8.02474806e-26,   2.65762802e-32,   8.00720594e-26,\n",
              "          2.25425071e-19,   2.61879518e-32,   2.61770124e-32,\n",
              "          7.85188489e-26,   2.61281114e-32,   7.84221061e-26,\n",
              "          2.18314140e-19,   2.60456378e-32,   7.82145369e-26,\n",
              "          2.17543112e-19,   9.31155825e-25,   2.54508460e-31,\n",
              "          2.51380725e-31,   2.48267705e-31,   5.22032815e-25,\n",
              "          2.49705101e-31,   2.46597023e-31,   5.07378405e-25,\n",
              "          2.43195851e-31,   5.02593349e-25,   7.14606474e-19,\n",
              "          2.49748398e-31,   2.46459394e-31,   5.05957129e-25,\n",
              "          2.42912149e-31,   4.99567460e-25,   6.61326215e-19,\n",
              "          2.39210265e-31,   4.90518959e-25,   6.40974728e-19,\n",
              "         -7.01001418e-26,   2.51810173e-31,   2.48124894e-31,\n",
              "          5.19992446e-25,   2.44238105e-31,   5.10402104e-25,\n",
              "          7.21289089e-19,   2.40248928e-31,   4.98487872e-25,\n",
              "          6.83047628e-19,  -2.70610885e-25,   2.36231013e-31,\n",
              "          4.85334112e-25,   6.13575603e-19,  -4.50707289e-25,\n",
              "          3.52445180e-32]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 39,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model4.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": [
              "0.7690138140531112"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 40,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Open', 'High', 'Low', 'Volume']].values\n",
        "y = df[['Adj_Close']].values\n",
        "poly = PolynomialFeatures(degree=4)\n",
        "poly_features = poly.fit_transform(X)\n",
        "poly.fit(X,y)\n",
        "poly_regression = LinearRegression()\n",
        "poly_regression.fit(poly_features,y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 41,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X[:,:-3]\n",
        "X.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 42,
          "data": {
            "text/plain": [
              "(178, 1)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 42,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the data for Plynomial Regression \n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X,poly_regression.predict(poly_features))\n",
        "poly = PolynomialFeatures(degree=4)\n",
        "poly_features = poly.fit_transform(X)\n",
        "poly.fit(X,y)\n",
        "poly_regression = LinearRegression()\n",
        "poly_regression.fit(poly_features,y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280faca8518>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 43,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the Linear Regression\n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X,regressor.predict(X))\n",
        "plt.title(\"Linear Regression\")\n",
        "plt.xlabel(\"Stock Features\")\n",
        "plt.ylabel(\"Stock Prices\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<matplotlib.figure.Figure at 0x280fb45d400>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 44,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
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