25_SimpleStockPredictionMachineLearning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Machine Learning Prediction"
      ],
      "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",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AAPL'\n",
        "start = '2018-01-01'\n",
        "end = '2019-01-31'\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2018-01-02  170.160004  172.300003  169.259995  172.259995  168.987320   \n",
              "2018-01-03  172.529999  174.550003  171.960007  172.229996  168.957886   \n",
              "2018-01-04  172.539993  173.470001  172.080002  173.029999  169.742706   \n",
              "2018-01-05  173.440002  175.369995  173.050003  175.000000  171.675278   \n",
              "2018-01-08  174.350006  175.610001  173.929993  174.350006  171.037628   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2018-01-02  25555900  \n",
              "2018-01-03  29517900  \n",
              "2018-01-04  22434600  \n",
              "2018-01-05  23660000  \n",
              "2018-01-08  20567800  "
            ],
            "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",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</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>2018-01-02</th>\n",
              "      <td>170.160004</td>\n",
              "      <td>172.300003</td>\n",
              "      <td>169.259995</td>\n",
              "      <td>172.259995</td>\n",
              "      <td>168.987320</td>\n",
              "      <td>25555900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>172.529999</td>\n",
              "      <td>174.550003</td>\n",
              "      <td>171.960007</td>\n",
              "      <td>172.229996</td>\n",
              "      <td>168.957886</td>\n",
              "      <td>29517900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>172.539993</td>\n",
              "      <td>173.470001</td>\n",
              "      <td>172.080002</td>\n",
              "      <td>173.029999</td>\n",
              "      <td>169.742706</td>\n",
              "      <td>22434600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>173.440002</td>\n",
              "      <td>175.369995</td>\n",
              "      <td>173.050003</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>171.675278</td>\n",
              "      <td>23660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>175.610001</td>\n",
              "      <td>173.929993</td>\n",
              "      <td>174.350006</td>\n",
              "      <td>171.037628</td>\n",
              "      <td>20567800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2019-01-25  155.479996  158.130005  154.320007  157.759995  157.086288   \n",
              "2019-01-28  155.789993  156.330002  153.660004  156.300003  155.632523   \n",
              "2019-01-29  156.250000  158.130005  154.110001  154.679993  154.019440   \n",
              "2019-01-30  163.250000  166.149994  160.229996  165.250000  164.544296   \n",
              "2019-01-31  166.110001  169.000000  164.559998  166.440002  165.729218   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2019-01-25  33535500  \n",
              "2019-01-28  26192100  \n",
              "2019-01-29  41587200  \n",
              "2019-01-30  61109800  \n",
              "2019-01-31  40739600  "
            ],
            "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",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</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>2019-01-25</th>\n",
              "      <td>155.479996</td>\n",
              "      <td>158.130005</td>\n",
              "      <td>154.320007</td>\n",
              "      <td>157.759995</td>\n",
              "      <td>157.086288</td>\n",
              "      <td>33535500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-28</th>\n",
              "      <td>155.789993</td>\n",
              "      <td>156.330002</td>\n",
              "      <td>153.660004</td>\n",
              "      <td>156.300003</td>\n",
              "      <td>155.632523</td>\n",
              "      <td>26192100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-29</th>\n",
              "      <td>156.250000</td>\n",
              "      <td>158.130005</td>\n",
              "      <td>154.110001</td>\n",
              "      <td>154.679993</td>\n",
              "      <td>154.019440</td>\n",
              "      <td>41587200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-30</th>\n",
              "      <td>163.250000</td>\n",
              "      <td>166.149994</td>\n",
              "      <td>160.229996</td>\n",
              "      <td>165.250000</td>\n",
              "      <td>164.544296</td>\n",
              "      <td>61109800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>166.110001</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>164.559998</td>\n",
              "      <td>166.440002</td>\n",
              "      <td>165.729218</td>\n",
              "      <td>40739600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Data\n",
        "df['Open_Close'] = (df['Open'] - df['Adj Close'])/df['Open']\n",
        "df['High_Low'] = (df['High'] - df['Low'])/df['Low']\n",
        "df['Increase_Decrease'] = np.where(df['Volume'].shift(-1) > df['Volume'],1,0)\n",
        "df['Buy_Sell_on_Open'] = np.where(df['Open'].shift(-1) > df['Open'],1,0)\n",
        "df['Buy_Sell'] = np.where(df['Adj Close'].shift(-1) > df['Adj Close'],1,0)\n",
        "df['Returns'] = df['Adj Close'].pct_change()\n",
        "df = df.dropna()\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2018-01-03  172.529999  174.550003  171.960007  172.229996  168.957886   \n",
              "2018-01-04  172.539993  173.470001  172.080002  173.029999  169.742706   \n",
              "2018-01-05  173.440002  175.369995  173.050003  175.000000  171.675278   \n",
              "2018-01-08  174.350006  175.610001  173.929993  174.350006  171.037628   \n",
              "2018-01-09  174.550003  175.059998  173.410004  174.330002  171.018005   \n",
              "\n",
              "              Volume  Open_Close  High_Low  Increase_Decrease  \\\n",
              "Date                                                            \n",
              "2018-01-03  29517900    0.020704  0.015062                  0   \n",
              "2018-01-04  22434600    0.016212  0.008078                  1   \n",
              "2018-01-05  23660000    0.010175  0.013406                  0   \n",
              "2018-01-08  20567800    0.018998  0.009659                  1   \n",
              "2018-01-09  21584000    0.020235  0.009515                  1   \n",
              "\n",
              "            Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "Date                                              \n",
              "2018-01-03                 1         1 -0.000174  \n",
              "2018-01-04                 1         1  0.004645  \n",
              "2018-01-05                 1         0  0.011385  \n",
              "2018-01-08                 1         0 -0.003714  \n",
              "2018-01-09                 0         0 -0.000115  "
            ],
            "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",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open_Close</th>\n",
              "      <th>High_Low</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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2018-01-03</th>\n",
              "      <td>172.529999</td>\n",
              "      <td>174.550003</td>\n",
              "      <td>171.960007</td>\n",
              "      <td>172.229996</td>\n",
              "      <td>168.957886</td>\n",
              "      <td>29517900</td>\n",
              "      <td>0.020704</td>\n",
              "      <td>0.015062</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.000174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>172.539993</td>\n",
              "      <td>173.470001</td>\n",
              "      <td>172.080002</td>\n",
              "      <td>173.029999</td>\n",
              "      <td>169.742706</td>\n",
              "      <td>22434600</td>\n",
              "      <td>0.016212</td>\n",
              "      <td>0.008078</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.004645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>173.440002</td>\n",
              "      <td>175.369995</td>\n",
              "      <td>173.050003</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>171.675278</td>\n",
              "      <td>23660000</td>\n",
              "      <td>0.010175</td>\n",
              "      <td>0.013406</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.011385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>175.610001</td>\n",
              "      <td>173.929993</td>\n",
              "      <td>174.350006</td>\n",
              "      <td>171.037628</td>\n",
              "      <td>20567800</td>\n",
              "      <td>0.018998</td>\n",
              "      <td>0.009659</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.003714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-09</th>\n",
              "      <td>174.550003</td>\n",
              "      <td>175.059998</td>\n",
              "      <td>173.410004</td>\n",
              "      <td>174.330002</td>\n",
              "      <td>171.018005</td>\n",
              "      <td>21584000</td>\n",
              "      <td>0.020235</td>\n",
              "      <td>0.009515</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.000115</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "(271, 12)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(df['Open']).reshape(271,-1)\n",
        "Y = np.array(df['Adj Close']).reshape(271,-1)"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Sklearn Machine Learning Library"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Linear Regression Model because is the simplest model"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model becasue it measure how well the machine learning model generalizes the data when is trained\n",
        "lr.fit(X, Y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Estimate intercept coefficient:', lr.intercept_)\n",
        "print('Number of coefficients:', len(lr.coef_))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimate intercept coefficient: [-0.96129428]\n",
            "Number of coefficients: 1\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Accuracy Score:', lr.score(X, Y))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.98579297029\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting the price\n",
        "lr.predict(X)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": [
              "array([[ 170.64668433],\n",
              "       [ 170.65662492],\n",
              "       [ 171.55182417],\n",
              "       [ 172.456965  ],\n",
              "       [ 172.65589319],\n",
              "       [ 171.27332251],\n",
              "       [ 172.69567246],\n",
              "       [ 174.27717234],\n",
              "       [ 175.98798145],\n",
              "       [ 174.24733365],\n",
              "       [ 177.45012659],\n",
              "       [ 176.69419409],\n",
              "       [ 175.39119687],\n",
              "       [ 175.39119687],\n",
              "       [ 175.34146109],\n",
              "       [ 172.616099  ],\n",
              "       [ 170.1195177 ],\n",
              "       [ 168.28935486],\n",
              "       [ 163.68409314],\n",
              "       [ 165.01692805],\n",
              "       [ 165.3153278 ],\n",
              "       [ 164.1515824 ],\n",
              "       [ 157.28846277],\n",
              "       [ 153.04127817],\n",
              "       [ 161.2571298 ],\n",
              "       [ 158.47209034],\n",
              "       [ 155.26931232],\n",
              "       [ 156.69166328],\n",
              "       [ 160.12322309],\n",
              "       [ 161.20739402],\n",
              "       [ 167.92132124],\n",
              "       [ 170.47759482],\n",
              "       [ 170.16925348],\n",
              "       [ 170.94508408],\n",
              "       [ 169.92058951],\n",
              "       [ 171.78059104],\n",
              "       [ 174.44627677],\n",
              "       [ 177.18158045],\n",
              "       [ 177.34071445],\n",
              "       [ 176.62456022],\n",
              "       [ 170.9152454 ],\n",
              "       [ 173.31237005],\n",
              "       [ 175.99793796],\n",
              "       [ 173.04380799],\n",
              "       [ 173.5809162 ],\n",
              "       [ 176.04767373],\n",
              "       [ 178.36520802],\n",
              "       [ 180.65291953],\n",
              "       [ 178.39506162],\n",
              "       [ 176.58478095],\n",
              "       [ 176.73397336],\n",
              "       [ 175.41109397],\n",
              "       [ 173.34220774],\n",
              "       [ 173.14326463],\n",
              "       [ 168.13020594],\n",
              "       [ 166.52880897],\n",
              "       [ 166.21052704],\n",
              "       [ 171.79053263],\n",
              "       [ 165.39490226],\n",
              "       [ 165.95190756],\n",
              "       [ 164.78816117],\n",
              "       [ 165.78281706],\n",
              "       [ 163.03757279],\n",
              "       [ 170.69642011],\n",
              "       [ 169.09502314],\n",
              "       [ 168.0108522 ],\n",
              "       [ 171.11417359],\n",
              "       [ 170.34828458],\n",
              "       [ 171.52198648],\n",
              "       [ 172.88466007],\n",
              "       [ 173.13332404],\n",
              "       [ 174.5855276 ],\n",
              "       [ 175.8984664 ],\n",
              "       [ 171.87010709],\n",
              "       [ 168.72700544],\n",
              "       [ 164.97714878],\n",
              "       [ 163.82334397],\n",
              "       [ 160.78964054],\n",
              "       [ 162.28162437],\n",
              "       [ 162.16227064],\n",
              "       [ 160.30226911],\n",
              "       [ 164.55939529],\n",
              "       [ 173.33225223],\n",
              "       [ 173.97878751],\n",
              "       [ 176.33611698],\n",
              "       [ 183.22907529],\n",
              "       [ 183.04010261],\n",
              "       [ 184.5917638 ],\n",
              "       [ 185.77540629],\n",
              "       [ 187.51605408],\n",
              "       [ 187.03860931],\n",
              "       [ 184.82053067],\n",
              "       [ 184.11433295],\n",
              "       [ 186.03401184],\n",
              "       [ 185.22834257],\n",
              "       [ 186.03401184],\n",
              "       [ 186.41198605],\n",
              "       [ 184.3928356 ],\n",
              "       [ 186.79990085],\n",
              "       [ 186.26277872],\n",
              "       [ 185.63615546],\n",
              "       [ 185.75550919],\n",
              "       [ 185.25818125],\n",
              "       [ 186.02407026],\n",
              "       [ 189.65455826],\n",
              "       [ 191.07692414],\n",
              "       [ 191.63392944],\n",
              "       [ 192.14119797],\n",
              "       [ 189.187069  ],\n",
              "       [ 189.36611502],\n",
              "       [ 189.40589429],\n",
              "       [ 190.43038886],\n",
              "       [ 189.56504321],\n",
              "       [ 188.05316229],\n",
              "       [ 185.91465811],\n",
              "       [ 183.18929502],\n",
              "       [ 184.3928356 ],\n",
              "       [ 185.28801993],\n",
              "       [ 184.16405381],\n",
              "       [ 181.45858881],\n",
              "       [ 181.05079084],\n",
              "       [ 183.27881107],\n",
              "       [ 182.15485986],\n",
              "       [ 184.33314332],\n",
              "       [ 181.87635721],\n",
              "       [ 185.82512714],\n",
              "       [ 183.30864975],\n",
              "       [ 183.46779767],\n",
              "       [ 187.52599567],\n",
              "       [ 188.72953625],\n",
              "       [ 186.53133978],\n",
              "       [ 187.55583435],\n",
              "       [ 189.09755395],\n",
              "       [ 189.53520453],\n",
              "       [ 187.77465964],\n",
              "       [ 189.79381009],\n",
              "       [ 187.71498228],\n",
              "       [ 189.79381009],\n",
              "       [ 188.69968265],\n",
              "       [ 190.46022754],\n",
              "       [ 191.06696863],\n",
              "       [ 192.60868823],\n",
              "       [ 192.98666144],\n",
              "       [ 189.91316382],\n",
              "       [ 188.32172336],\n",
              "       [ 197.1045368 ],\n",
              "       [ 198.54678485],\n",
              "       [ 204.96231231],\n",
              "       [ 205.92712952],\n",
              "       [ 207.24008225],\n",
              "       [ 203.98755353],\n",
              "       [ 205.21097629],\n",
              "       [ 205.29055075],\n",
              "       [ 205.62872977],\n",
              "       [ 208.0755902 ],\n",
              "       [ 207.14061069],\n",
              "       [ 209.65708908],\n",
              "       [ 211.33805951],\n",
              "       [ 215.97315991],\n",
              "       [ 214.68010428],\n",
              "       [ 211.99453637],\n",
              "       [ 212.54158517],\n",
              "       [ 214.48117608],\n",
              "       [ 215.02822488],\n",
              "       [ 216.87828582],\n",
              "       [ 218.01219253],\n",
              "       [ 221.09563174],\n",
              "       [ 224.33820495],\n",
              "       [ 226.22806008],\n",
              "       [ 226.80496149],\n",
              "       [ 224.0597023 ],\n",
              "       [ 219.70311947],\n",
              "       [ 218.80792023],\n",
              "       [ 215.88362994],\n",
              "       [ 222.77660217],\n",
              "       [ 221.36419281],\n",
              "       [ 223.58227145],\n",
              "       [ 220.0015043 ],\n",
              "       [ 215.66480365],\n",
              "       [ 216.37101629],\n",
              "       [ 218.10172251],\n",
              "       [ 218.63883071],\n",
              "       [ 214.70000137],\n",
              "       [ 217.61433615],\n",
              "       [ 218.857656  ],\n",
              "       [ 221.66259256],\n",
              "       [ 222.62739484],\n",
              "       [ 225.77051141],\n",
              "       [ 225.07425528],\n",
              "       [ 227.85929473],\n",
              "       [ 228.58538955],\n",
              "       [ 225.78046792],\n",
              "       [ 220.06119659],\n",
              "       [ 221.48354654],\n",
              "       [ 223.29382821],\n",
              "       [ 212.41228986],\n",
              "       [ 218.2807536 ],\n",
              "       [ 219.01680492],\n",
              "       [ 216.79871136],\n",
              "       [ 220.15071164],\n",
              "       [ 215.73443752],\n",
              "       [ 215.93336572],\n",
              "       [ 217.65411542],\n",
              "       [ 213.71528707],\n",
              "       [ 220.44911139],\n",
              "       [ 215.58524511],\n",
              "       [ 213.78490503],\n",
              "       [ 217.05733084],\n",
              "       [ 209.06028958],\n",
              "       [ 214.75967874],\n",
              "       [ 216.91808001],\n",
              "       [ 207.46884912],\n",
              "       [ 202.24690573],\n",
              "       [ 199.87961975],\n",
              "       [ 203.90797907],\n",
              "       [ 207.89654419],\n",
              "       [ 203.49022559],\n",
              "       [ 196.97522656],\n",
              "       [ 189.64461767],\n",
              "       [ 191.90247559],\n",
              "       [ 186.42192664],\n",
              "       [ 188.52065155],\n",
              "       [ 188.02332361],\n",
              "       [ 176.45547071],\n",
              "       [ 177.80820371],\n",
              "       [ 173.04380799],\n",
              "       [ 172.34755186],\n",
              "       [ 169.63213135],\n",
              "       [ 174.82423606],\n",
              "       [ 180.7225534 ],\n",
              "       [ 178.36520802],\n",
              "       [ 182.51293698],\n",
              "       [ 179.02168488],\n",
              "       [ 169.88079532],\n",
              "       [ 171.60155994],\n",
              "       [ 163.15692652],\n",
              "       [ 169.78133868],\n",
              "       [ 168.52806232],\n",
              "       [ 168.61759229],\n",
              "       [ 167.13555005],\n",
              "       [ 163.60451868],\n",
              "       [ 163.53490073],\n",
              "       [ 164.1515824 ],\n",
              "       [ 158.58150349],\n",
              "       [ 155.06042862],\n",
              "       [ 146.39696891],\n",
              "       [ 146.54617625],\n",
              "       [ 154.04587465],\n",
              "       [ 155.69700739],\n",
              "       [ 156.72150196],\n",
              "       [ 153.10095454],\n",
              "       [ 142.24925587],\n",
              "       [ 142.79631959],\n",
              "       [ 146.94403263],\n",
              "       [ 147.79943769],\n",
              "       [ 149.52018739],\n",
              "       [ 150.72372797],\n",
              "       [ 151.10170218],\n",
              "       [ 149.08255173],\n",
              "       [ 148.50564933],\n",
              "       [ 151.30063038],\n",
              "       [ 152.41463999],\n",
              "       [ 155.69700739],\n",
              "       [ 154.61283646],\n",
              "       [ 152.36490421],\n",
              "       [ 152.32512494],\n",
              "       [ 153.68779853],\n",
              "       [ 153.99613887],\n",
              "       [ 154.45368754],\n",
              "       [ 161.41627872],\n",
              "       [ 164.26099554]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.scatter(df['Adj Close'], lr.predict(X))\n",
        "plt.plot(X, lr.predict(X), color = 'red')\n",
        "plt.xlabel('Prices')\n",
        "plt.ylabel('Predicted Prices')\n",
        "plt.grid()\n",
        "plt.title('Prices vs Predicted Prices')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": [
              "Text(0.5,1,'Prices vs Predicted Prices')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "file_extension": ".py",
      "nbconvert_exporter": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "version": "3.5.5",
      "pygments_lexer": "ipython3",
      "mimetype": "text/x-python"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.14.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}