Linear Regression and LSTM model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "#from stocker import Stocker\n",
    "import io\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader.data as web\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import datetime\n",
    "from matplotlib import style\n",
    "from pandas import read_csv\n",
    "from pandas.util.testing import assert_frame_equal\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "plt.style.use('fivethirtyeight')\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/akashghosh/Desktop/AirlinesData\n"
     ]
    }
   ],
   "source": [
    "cd desktop/AirlinesData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the csv data \n",
    "Airchina_df = pd.read_csv('Airchina.csv')\n",
    "Lufthansa_df = pd.read_csv('Lufthansa.csv')\n",
    "American_df = pd.read_csv('American.csv')\n",
    "Delta_df = pd.read_csv('Delta.csv')\n",
    "Qantas_df = pd.read_csv('Qantas.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>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>2019-11-04</td>\n",
       "      <td>56.750000</td>\n",
       "      <td>58.430000</td>\n",
       "      <td>56.419998</td>\n",
       "      <td>57.580002</td>\n",
       "      <td>57.185318</td>\n",
       "      <td>30775300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>57.259998</td>\n",
       "      <td>57.689999</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.849998</td>\n",
       "      <td>56.460320</td>\n",
       "      <td>20460900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-11-18</td>\n",
       "      <td>56.779999</td>\n",
       "      <td>57.490002</td>\n",
       "      <td>55.119999</td>\n",
       "      <td>56.110001</td>\n",
       "      <td>55.725391</td>\n",
       "      <td>30546100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>56.389999</td>\n",
       "      <td>57.849998</td>\n",
       "      <td>55.689999</td>\n",
       "      <td>57.310001</td>\n",
       "      <td>56.917168</td>\n",
       "      <td>18426600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>57.250000</td>\n",
       "      <td>57.650002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>56.139999</td>\n",
       "      <td>55.755184</td>\n",
       "      <td>25728100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>55.990002</td>\n",
       "      <td>57.490002</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>56.730000</td>\n",
       "      <td>56.341141</td>\n",
       "      <td>27781600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>57.169998</td>\n",
       "      <td>59.639999</td>\n",
       "      <td>57.150002</td>\n",
       "      <td>58.970001</td>\n",
       "      <td>58.565788</td>\n",
       "      <td>29632200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>59.169998</td>\n",
       "      <td>59.919998</td>\n",
       "      <td>58.790001</td>\n",
       "      <td>59.080002</td>\n",
       "      <td>58.675037</td>\n",
       "      <td>10727100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>59.389999</td>\n",
       "      <td>56.910000</td>\n",
       "      <td>58.060001</td>\n",
       "      <td>57.662025</td>\n",
       "      <td>20233900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>59.490002</td>\n",
       "      <td>56.660000</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.833939</td>\n",
       "      <td>33202700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>59.029999</td>\n",
       "      <td>62.480000</td>\n",
       "      <td>59.029999</td>\n",
       "      <td>62.029999</td>\n",
       "      <td>61.604813</td>\n",
       "      <td>43542000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>61.610001</td>\n",
       "      <td>57.840000</td>\n",
       "      <td>58.810001</td>\n",
       "      <td>58.406887</td>\n",
       "      <td>42452400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>56.490002</td>\n",
       "      <td>57.970001</td>\n",
       "      <td>55.430000</td>\n",
       "      <td>55.740002</td>\n",
       "      <td>55.357929</td>\n",
       "      <td>40397400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>56.200001</td>\n",
       "      <td>60.029999</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>57.919998</td>\n",
       "      <td>57.522984</td>\n",
       "      <td>29493800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>57.540001</td>\n",
       "      <td>59.919998</td>\n",
       "      <td>57.430000</td>\n",
       "      <td>58.900002</td>\n",
       "      <td>58.496269</td>\n",
       "      <td>22498300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>58.869999</td>\n",
       "      <td>59.470001</td>\n",
       "      <td>56.869999</td>\n",
       "      <td>57.869999</td>\n",
       "      <td>57.473328</td>\n",
       "      <td>21625500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>55.380001</td>\n",
       "      <td>45.029999</td>\n",
       "      <td>46.130001</td>\n",
       "      <td>46.130001</td>\n",
       "      <td>91615600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>46.220001</td>\n",
       "      <td>49.970001</td>\n",
       "      <td>42.869999</td>\n",
       "      <td>45.889999</td>\n",
       "      <td>45.889999</td>\n",
       "      <td>115552700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>43.040001</td>\n",
       "      <td>45.880001</td>\n",
       "      <td>33.459999</td>\n",
       "      <td>33.709999</td>\n",
       "      <td>33.709999</td>\n",
       "      <td>114351900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date       Open       High        Low      Close  Adj Close  \\\n",
       "0   2019-11-04  56.750000  58.430000  56.419998  57.580002  57.185318   \n",
       "1   2019-11-11  57.259998  57.689999  56.070000  56.849998  56.460320   \n",
       "2   2019-11-18  56.779999  57.490002  55.119999  56.110001  55.725391   \n",
       "3   2019-11-25  56.389999  57.849998  55.689999  57.310001  56.917168   \n",
       "4   2019-12-02  57.250000  57.650002  54.540001  56.139999  55.755184   \n",
       "5   2019-12-09  55.990002  57.490002  54.950001  56.730000  56.341141   \n",
       "6   2019-12-16  57.169998  59.639999  57.150002  58.970001  58.565788   \n",
       "7   2019-12-23  59.169998  59.919998  58.790001  59.080002  58.675037   \n",
       "8   2019-12-30  59.250000  59.389999  56.910000  58.060001  57.662025   \n",
       "9   2020-01-06  56.990002  59.490002  56.660000  59.240002  58.833939   \n",
       "10  2020-01-13  59.029999  62.480000  59.029999  62.029999  61.604813   \n",
       "11  2020-01-20  61.250000  61.610001  57.840000  58.810001  58.406887   \n",
       "12  2020-01-27  56.490002  57.970001  55.430000  55.740002  55.357929   \n",
       "13  2020-02-03  56.200001  60.029999  56.000000  57.919998  57.522984   \n",
       "14  2020-02-10  57.540001  59.919998  57.430000  58.900002  58.496269   \n",
       "15  2020-02-17  58.869999  59.470001  56.869999  57.869999  57.473328   \n",
       "16  2020-02-24  54.830002  55.380001  45.029999  46.130001  46.130001   \n",
       "17  2020-03-02  46.220001  49.970001  42.869999  45.889999  45.889999   \n",
       "18  2020-03-09  43.040001  45.880001  33.459999  33.709999  33.709999   \n",
       "\n",
       "       Volume  \n",
       "0    30775300  \n",
       "1    20460900  \n",
       "2    30546100  \n",
       "3    18426600  \n",
       "4    25728100  \n",
       "5    27781600  \n",
       "6    29632200  \n",
       "7    10727100  \n",
       "8    20233900  \n",
       "9    33202700  \n",
       "10   43542000  \n",
       "11   42452400  \n",
       "12   40397400  \n",
       "13   29493800  \n",
       "14   22498300  \n",
       "15   21625500  \n",
       "16   91615600  \n",
       "17  115552700  \n",
       "18  114351900  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using delta airlines data for the prediction\n",
    "Delta_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using LSTM for Delta\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)\n",
    "# fix random seed for reproducibility\n",
    "np.random.seed(7)\n",
    "# load the dataset\n",
    "dataframe = read_csv('Delta.csv', usecols=[5], engine='python')\n",
    "dataset = dataframe.values\n",
    "dataset = dataset.astype('float32')\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "look_back = 3\n",
    "trainX, trainY = create_dataset(dataset, look_back)\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0],1, trainX.shape[1]))\n",
    "# create and fit the LSTM network\n",
    "#trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "15/15 [==============================] - 1s 52ms/step - loss: 0.6823\n",
      "Epoch 2/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5807\n",
      "Epoch 3/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.4842\n",
      "Epoch 4/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.3950\n",
      "Epoch 5/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.3127\n",
      "Epoch 6/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.2388\n",
      "Epoch 7/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.1774\n",
      "Epoch 8/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.1247\n",
      "Epoch 9/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0860\n",
      "Epoch 10/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0602\n",
      "Epoch 11/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0418\n",
      "Epoch 12/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0308\n",
      "Epoch 13/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0262\n",
      "Epoch 14/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0234\n",
      "Epoch 15/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0219\n",
      "Epoch 16/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0216\n",
      "Epoch 17/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0214\n",
      "Epoch 18/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0218\n",
      "Epoch 19/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0212\n",
      "Epoch 20/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0212\n",
      "Epoch 21/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0216\n",
      "Epoch 22/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0214\n",
      "Epoch 23/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0214\n",
      "Epoch 24/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0213\n",
      "Epoch 25/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0213\n",
      "Epoch 26/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0212\n",
      "Epoch 27/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0211\n",
      "Epoch 28/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0211\n",
      "Epoch 29/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0212\n",
      "Epoch 30/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0211\n",
      "Epoch 31/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0211\n",
      "Epoch 32/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0210\n",
      "Epoch 33/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0211\n",
      "Epoch 34/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0212\n",
      "Epoch 35/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0208\n",
      "Epoch 36/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0210\n",
      "Epoch 37/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0208\n",
      "Epoch 38/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0211\n",
      "Epoch 39/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0210\n",
      "Epoch 40/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0208\n",
      "Epoch 41/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0208\n",
      "Epoch 42/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0206\n",
      "Epoch 43/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0207\n",
      "Epoch 44/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0206\n",
      "Epoch 45/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0214\n",
      "Epoch 46/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0207\n",
      "Epoch 47/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0209\n",
      "Epoch 48/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0206\n",
      "Epoch 49/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0207\n",
      "Epoch 50/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0206\n",
      "Epoch 51/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0204\n",
      "Epoch 52/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0206\n",
      "Epoch 53/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0203\n",
      "Epoch 54/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0204\n",
      "Epoch 55/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0204\n",
      "Epoch 56/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0206\n",
      "Epoch 57/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0202\n",
      "Epoch 58/100\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0204\n",
      "Epoch 59/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0202\n",
      "Epoch 60/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0203\n",
      "Epoch 61/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0203\n",
      "Epoch 62/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0204\n",
      "Epoch 63/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0206\n",
      "Epoch 64/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0202\n",
      "Epoch 65/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0202\n",
      "Epoch 66/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0201\n",
      "Epoch 67/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0199\n",
      "Epoch 68/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0207\n",
      "Epoch 69/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0198\n",
      "Epoch 70/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0200\n",
      "Epoch 71/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0204\n",
      "Epoch 72/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0202\n",
      "Epoch 73/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0199\n",
      "Epoch 74/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0201\n",
      "Epoch 75/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0198\n",
      "Epoch 76/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0198\n",
      "Epoch 77/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0197\n",
      "Epoch 78/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0199\n",
      "Epoch 79/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0205\n",
      "Epoch 80/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0196\n",
      "Epoch 81/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0196\n",
      "Epoch 82/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0199\n",
      "Epoch 83/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0202\n",
      "Epoch 84/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0196\n",
      "Epoch 85/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0195\n",
      "Epoch 86/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0199\n",
      "Epoch 87/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0197\n",
      "Epoch 88/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0198\n",
      "Epoch 89/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0192\n",
      "Epoch 90/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0197\n",
      "Epoch 91/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0198\n",
      "Epoch 92/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0199\n",
      "Epoch 93/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0197\n",
      "Epoch 94/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0192\n",
      "Epoch 95/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0194\n",
      "Epoch 96/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0192\n",
      "Epoch 97/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0190\n",
      "Epoch 98/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0193\n",
      "Epoch 99/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0191\n",
      "Epoch 100/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0190\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x13a160e48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(6, input_shape=(1, look_back)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[53.32353]]\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX[-1].reshape(1,1,3))\n",
    "#testPredict = model.predict(testX)\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "print(trainPredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using LSTM for Qantas\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)\n",
    "# fix random seed for reproducibility\n",
    "np.random.seed(7)\n",
    "# load the dataset\n",
    "dataframe = read_csv('Qantas.csv', usecols=[5], engine='python')\n",
    "dataset = dataframe.values\n",
    "dataset = dataset.astype('float32')\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "look_back = 3\n",
    "trainX, trainY = create_dataset(dataset, look_back)\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0],1, trainX.shape[1]))\n",
    "# create and fit the LSTM network\n",
    "#trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 0.4580\n",
      "Epoch 2/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.3894\n",
      "Epoch 3/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.3245\n",
      "Epoch 4/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.2607\n",
      "Epoch 5/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.2054\n",
      "Epoch 6/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.1545\n",
      "Epoch 7/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.1153\n",
      "Epoch 8/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0811\n",
      "Epoch 9/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0564\n",
      "Epoch 10/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0423\n",
      "Epoch 11/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0309\n",
      "Epoch 12/100\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0243\n",
      "Epoch 13/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0219\n",
      "Epoch 14/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0197\n",
      "Epoch 15/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0189\n",
      "Epoch 16/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0187\n",
      "Epoch 17/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0185\n",
      "Epoch 18/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0187\n",
      "Epoch 19/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0183\n",
      "Epoch 20/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0183\n",
      "Epoch 21/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0187\n",
      "Epoch 22/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0183\n",
      "Epoch 23/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0181\n",
      "Epoch 24/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0180\n",
      "Epoch 25/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0182\n",
      "Epoch 26/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0179\n",
      "Epoch 27/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0178\n",
      "Epoch 28/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0178\n",
      "Epoch 29/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0179\n",
      "Epoch 30/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0177\n",
      "Epoch 31/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0176\n",
      "Epoch 32/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0176\n",
      "Epoch 33/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0177\n",
      "Epoch 34/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0180\n",
      "Epoch 35/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0173\n",
      "Epoch 36/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0173\n",
      "Epoch 37/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0172\n",
      "Epoch 38/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0172\n",
      "Epoch 39/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0172\n",
      "Epoch 40/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0171\n",
      "Epoch 41/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0170\n",
      "Epoch 42/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0169\n",
      "Epoch 43/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0170\n",
      "Epoch 44/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0168\n",
      "Epoch 45/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0170\n",
      "Epoch 46/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0168\n",
      "Epoch 47/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0167\n",
      "Epoch 48/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0165\n",
      "Epoch 49/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0165\n",
      "Epoch 50/100\n",
      "15/15 [==============================] - ETA: 0s - loss: 0.004 - 0s 3ms/step - loss: 0.0165\n",
      "Epoch 51/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0163\n",
      "Epoch 52/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0163\n",
      "Epoch 53/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0161\n",
      "Epoch 54/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0164\n",
      "Epoch 55/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0162\n",
      "Epoch 56/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0162\n",
      "Epoch 57/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0159\n",
      "Epoch 58/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0158\n",
      "Epoch 59/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0158\n",
      "Epoch 60/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0157\n",
      "Epoch 61/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0157\n",
      "Epoch 62/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0156\n",
      "Epoch 63/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0156\n",
      "Epoch 64/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0155\n",
      "Epoch 65/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0154\n",
      "Epoch 66/100\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0153\n",
      "Epoch 67/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0152\n",
      "Epoch 68/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0156\n",
      "Epoch 69/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0150\n",
      "Epoch 70/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0153\n",
      "Epoch 71/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0149\n",
      "Epoch 72/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0150\n",
      "Epoch 73/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0150\n",
      "Epoch 74/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0150\n",
      "Epoch 75/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0148\n",
      "Epoch 76/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0147\n",
      "Epoch 77/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0146\n",
      "Epoch 78/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0146\n",
      "Epoch 79/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0149\n",
      "Epoch 80/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0144\n",
      "Epoch 81/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0143\n",
      "Epoch 82/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0143\n",
      "Epoch 83/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0143\n",
      "Epoch 84/100\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0142\n",
      "Epoch 85/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0140\n",
      "Epoch 86/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0142\n",
      "Epoch 87/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0139\n",
      "Epoch 88/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0139\n",
      "Epoch 89/100\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0137\n",
      "Epoch 90/100\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0140\n",
      "Epoch 91/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0142\n",
      "Epoch 92/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0140\n",
      "Epoch 93/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0139\n",
      "Epoch 94/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0135\n",
      "Epoch 95/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0134\n",
      "Epoch 96/100\n",
      "15/15 [==============================] - ETA: 0s - loss: 0.018 - 0s 3ms/step - loss: 0.0133\n",
      "Epoch 97/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0132\n",
      "Epoch 98/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0132\n",
      "Epoch 99/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0131\n",
      "Epoch 100/100\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0130\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x13a96e4e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(6, input_shape=(1, look_back)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using LSTM for Lufthansa\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)\n",
    "# fix random seed for reproducibility\n",
    "np.random.seed(7)\n",
    "# load the dataset\n",
    "dataframe = read_csv('Lufthansa.csv', usecols=[5], engine='python')\n",
    "dataset = dataframe.values\n",
    "dataset = dataset.astype('float32')\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "look_back = 3\n",
    "trainX, trainY = create_dataset(dataset, look_back)\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0],1, trainX.shape[1]))\n",
    "# create and fit the LSTM network\n",
    "#trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14.776471]]\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX[-1].reshape(1,1,3))\n",
    "#testPredict = model.predict(testX)\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "print(trainPredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using LSTM for Qantas\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)\n",
    "# fix random seed for reproducibility\n",
    "np.random.seed(7)\n",
    "# load the dataset\n",
    "dataframe = read_csv('Qantas.csv', usecols=[5], engine='python')\n",
    "dataset = dataframe.values\n",
    "dataset = dataset.astype('float32')\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "look_back = 3\n",
    "trainX, trainY = create_dataset(dataset, look_back)\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0],1, trainX.shape[1]))\n",
    "# create and fit the LSTM network\n",
    "#trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18.994837]]\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX[-1].reshape(1,1,3))\n",
    "#testPredict = model.predict(testX)\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "print(trainPredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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>2019-09-09</th>\n",
       "      <td>58.450001</td>\n",
       "      <td>57.560001</td>\n",
       "      <td>57.980000</td>\n",
       "      <td>58.110001</td>\n",
       "      <td>5740500.0</td>\n",
       "      <td>57.286106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-10</th>\n",
       "      <td>58.720001</td>\n",
       "      <td>57.799999</td>\n",
       "      <td>58.080002</td>\n",
       "      <td>58.639999</td>\n",
       "      <td>4409600.0</td>\n",
       "      <td>57.808586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-11</th>\n",
       "      <td>59.180000</td>\n",
       "      <td>58.139999</td>\n",
       "      <td>58.619999</td>\n",
       "      <td>58.779999</td>\n",
       "      <td>4163700.0</td>\n",
       "      <td>57.946606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-12</th>\n",
       "      <td>59.889999</td>\n",
       "      <td>58.779999</td>\n",
       "      <td>58.779999</td>\n",
       "      <td>59.439999</td>\n",
       "      <td>5188700.0</td>\n",
       "      <td>58.597248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-13</th>\n",
       "      <td>60.580002</td>\n",
       "      <td>59.610001</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.009998</td>\n",
       "      <td>7548400.0</td>\n",
       "      <td>59.159164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-16</th>\n",
       "      <td>59.130001</td>\n",
       "      <td>57.250000</td>\n",
       "      <td>58.029999</td>\n",
       "      <td>59.070000</td>\n",
       "      <td>9556400.0</td>\n",
       "      <td>58.232494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-17</th>\n",
       "      <td>59.770000</td>\n",
       "      <td>58.160000</td>\n",
       "      <td>58.310001</td>\n",
       "      <td>59.369999</td>\n",
       "      <td>8176700.0</td>\n",
       "      <td>58.528240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-18</th>\n",
       "      <td>59.500000</td>\n",
       "      <td>58.669998</td>\n",
       "      <td>59.450001</td>\n",
       "      <td>59.310001</td>\n",
       "      <td>4075600.0</td>\n",
       "      <td>58.469090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-19</th>\n",
       "      <td>59.250000</td>\n",
       "      <td>58.310001</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>58.689999</td>\n",
       "      <td>3569900.0</td>\n",
       "      <td>57.857876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-20</th>\n",
       "      <td>59.180000</td>\n",
       "      <td>58.490002</td>\n",
       "      <td>58.799999</td>\n",
       "      <td>58.630001</td>\n",
       "      <td>6139400.0</td>\n",
       "      <td>57.798733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-23</th>\n",
       "      <td>58.660000</td>\n",
       "      <td>57.840000</td>\n",
       "      <td>58.310001</td>\n",
       "      <td>58.020000</td>\n",
       "      <td>4387100.0</td>\n",
       "      <td>57.197380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-24</th>\n",
       "      <td>58.910000</td>\n",
       "      <td>57.560001</td>\n",
       "      <td>58.619999</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>5047700.0</td>\n",
       "      <td>57.177662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-25</th>\n",
       "      <td>58.490002</td>\n",
       "      <td>57.680000</td>\n",
       "      <td>58.040001</td>\n",
       "      <td>58.310001</td>\n",
       "      <td>3193100.0</td>\n",
       "      <td>57.483269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-26</th>\n",
       "      <td>58.810001</td>\n",
       "      <td>57.930000</td>\n",
       "      <td>58.110001</td>\n",
       "      <td>58.779999</td>\n",
       "      <td>3795400.0</td>\n",
       "      <td>57.946606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-27</th>\n",
       "      <td>59.380001</td>\n",
       "      <td>58.160000</td>\n",
       "      <td>58.970001</td>\n",
       "      <td>58.279999</td>\n",
       "      <td>4395800.0</td>\n",
       "      <td>57.453693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>58.689999</td>\n",
       "      <td>57.520000</td>\n",
       "      <td>58.509998</td>\n",
       "      <td>57.599998</td>\n",
       "      <td>5208200.0</td>\n",
       "      <td>56.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>58.680000</td>\n",
       "      <td>56.650002</td>\n",
       "      <td>58.049999</td>\n",
       "      <td>57.009998</td>\n",
       "      <td>7102200.0</td>\n",
       "      <td>56.201698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-02</th>\n",
       "      <td>55.750000</td>\n",
       "      <td>52.869999</td>\n",
       "      <td>55.740002</td>\n",
       "      <td>54.349998</td>\n",
       "      <td>13770400.0</td>\n",
       "      <td>53.579414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-03</th>\n",
       "      <td>53.860001</td>\n",
       "      <td>51.810001</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>52.830002</td>\n",
       "      <td>14346800.0</td>\n",
       "      <td>52.080967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-04</th>\n",
       "      <td>53.959999</td>\n",
       "      <td>52.680000</td>\n",
       "      <td>52.849998</td>\n",
       "      <td>53.810001</td>\n",
       "      <td>6620900.0</td>\n",
       "      <td>53.047070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-07</th>\n",
       "      <td>54.110001</td>\n",
       "      <td>53.240002</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>53.360001</td>\n",
       "      <td>8659800.0</td>\n",
       "      <td>52.603451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>54.099998</td>\n",
       "      <td>52.889999</td>\n",
       "      <td>53.020000</td>\n",
       "      <td>53.200001</td>\n",
       "      <td>8039400.0</td>\n",
       "      <td>52.445721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>54.400002</td>\n",
       "      <td>53.590000</td>\n",
       "      <td>54.009998</td>\n",
       "      <td>53.919998</td>\n",
       "      <td>7615300.0</td>\n",
       "      <td>53.155510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>53.259998</td>\n",
       "      <td>51.070000</td>\n",
       "      <td>51.950001</td>\n",
       "      <td>53.099998</td>\n",
       "      <td>22703500.0</td>\n",
       "      <td>52.347134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>53.770000</td>\n",
       "      <td>52.639999</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>52.950001</td>\n",
       "      <td>10935000.0</td>\n",
       "      <td>52.199261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>53.200001</td>\n",
       "      <td>51.970001</td>\n",
       "      <td>52.049999</td>\n",
       "      <td>52.990002</td>\n",
       "      <td>6631900.0</td>\n",
       "      <td>52.238701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>53.990002</td>\n",
       "      <td>53.009998</td>\n",
       "      <td>53.209999</td>\n",
       "      <td>53.639999</td>\n",
       "      <td>8413300.0</td>\n",
       "      <td>52.879482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-16</th>\n",
       "      <td>54.130001</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>53.169998</td>\n",
       "      <td>5532800.0</td>\n",
       "      <td>52.416142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-17</th>\n",
       "      <td>53.759998</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>53.520000</td>\n",
       "      <td>53.369999</td>\n",
       "      <td>4402000.0</td>\n",
       "      <td>52.613308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-18</th>\n",
       "      <td>53.990002</td>\n",
       "      <td>53.180000</td>\n",
       "      <td>53.189999</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>6966400.0</td>\n",
       "      <td>53.066788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>57.180000</td>\n",
       "      <td>55.619999</td>\n",
       "      <td>56.490002</td>\n",
       "      <td>56.830002</td>\n",
       "      <td>13090200.0</td>\n",
       "      <td>56.440460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>57.970001</td>\n",
       "      <td>56.750000</td>\n",
       "      <td>57.549999</td>\n",
       "      <td>57.480000</td>\n",
       "      <td>6343700.0</td>\n",
       "      <td>57.086002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>57.799999</td>\n",
       "      <td>56.570000</td>\n",
       "      <td>57.639999</td>\n",
       "      <td>56.650002</td>\n",
       "      <td>5662000.0</td>\n",
       "      <td>56.261692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>57.180000</td>\n",
       "      <td>55.650002</td>\n",
       "      <td>55.650002</td>\n",
       "      <td>57.099998</td>\n",
       "      <td>5980700.0</td>\n",
       "      <td>56.708603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>57.080002</td>\n",
       "      <td>55.430000</td>\n",
       "      <td>56.700001</td>\n",
       "      <td>55.740002</td>\n",
       "      <td>9320800.0</td>\n",
       "      <td>55.357929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>57.250000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.200001</td>\n",
       "      <td>56.040001</td>\n",
       "      <td>7499000.0</td>\n",
       "      <td>55.655872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>58.400002</td>\n",
       "      <td>57.150002</td>\n",
       "      <td>57.200001</td>\n",
       "      <td>57.930000</td>\n",
       "      <td>6880800.0</td>\n",
       "      <td>57.532917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>59.299999</td>\n",
       "      <td>58.599998</td>\n",
       "      <td>58.970001</td>\n",
       "      <td>58.889999</td>\n",
       "      <td>6067000.0</td>\n",
       "      <td>58.486336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>60.029999</td>\n",
       "      <td>58.020000</td>\n",
       "      <td>59.799999</td>\n",
       "      <td>58.180000</td>\n",
       "      <td>5210200.0</td>\n",
       "      <td>57.781204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>58.250000</td>\n",
       "      <td>57.520000</td>\n",
       "      <td>57.959999</td>\n",
       "      <td>57.919998</td>\n",
       "      <td>3836800.0</td>\n",
       "      <td>57.522984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>58.119999</td>\n",
       "      <td>57.430000</td>\n",
       "      <td>57.540001</td>\n",
       "      <td>57.709999</td>\n",
       "      <td>4541100.0</td>\n",
       "      <td>57.314423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>59.099998</td>\n",
       "      <td>57.970001</td>\n",
       "      <td>58.099998</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>4566500.0</td>\n",
       "      <td>58.257912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>59.919998</td>\n",
       "      <td>59.180000</td>\n",
       "      <td>59.290001</td>\n",
       "      <td>59.470001</td>\n",
       "      <td>5227500.0</td>\n",
       "      <td>59.062363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13</th>\n",
       "      <td>59.209999</td>\n",
       "      <td>58.349998</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>59.130001</td>\n",
       "      <td>4353100.0</td>\n",
       "      <td>58.724693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-14</th>\n",
       "      <td>59.470001</td>\n",
       "      <td>58.580002</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.900002</td>\n",
       "      <td>3810100.0</td>\n",
       "      <td>58.496269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>59.470001</td>\n",
       "      <td>58.410000</td>\n",
       "      <td>58.869999</td>\n",
       "      <td>58.720001</td>\n",
       "      <td>5690600.0</td>\n",
       "      <td>58.317501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>58.580002</td>\n",
       "      <td>58.029999</td>\n",
       "      <td>58.490002</td>\n",
       "      <td>58.509998</td>\n",
       "      <td>4399900.0</td>\n",
       "      <td>58.509998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>58.990002</td>\n",
       "      <td>58.119999</td>\n",
       "      <td>58.200001</td>\n",
       "      <td>58.380001</td>\n",
       "      <td>5239400.0</td>\n",
       "      <td>58.380001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>58.020000</td>\n",
       "      <td>56.869999</td>\n",
       "      <td>57.959999</td>\n",
       "      <td>57.869999</td>\n",
       "      <td>6295600.0</td>\n",
       "      <td>57.869999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>55.380001</td>\n",
       "      <td>53.340000</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>54.230000</td>\n",
       "      <td>17983100.0</td>\n",
       "      <td>54.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>54.709999</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>50.889999</td>\n",
       "      <td>15836500.0</td>\n",
       "      <td>50.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>51.770000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>51.439999</td>\n",
       "      <td>49.590000</td>\n",
       "      <td>15985900.0</td>\n",
       "      <td>49.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>50.980000</td>\n",
       "      <td>45.029999</td>\n",
       "      <td>46.720001</td>\n",
       "      <td>48.189999</td>\n",
       "      <td>21949100.0</td>\n",
       "      <td>48.189999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>47.790001</td>\n",
       "      <td>45.139999</td>\n",
       "      <td>46.020000</td>\n",
       "      <td>46.130001</td>\n",
       "      <td>19861000.0</td>\n",
       "      <td>46.130001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>47.250000</td>\n",
       "      <td>44.330002</td>\n",
       "      <td>46.220001</td>\n",
       "      <td>47.160000</td>\n",
       "      <td>20098700.0</td>\n",
       "      <td>47.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>49.970001</td>\n",
       "      <td>46.040001</td>\n",
       "      <td>49.810001</td>\n",
       "      <td>46.180000</td>\n",
       "      <td>26344100.0</td>\n",
       "      <td>46.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>48.639999</td>\n",
       "      <td>46.020000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>20381800.0</td>\n",
       "      <td>48.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>46.570000</td>\n",
       "      <td>44.419998</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>45.009998</td>\n",
       "      <td>22918800.0</td>\n",
       "      <td>45.009998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>46.930000</td>\n",
       "      <td>42.869999</td>\n",
       "      <td>43.470001</td>\n",
       "      <td>45.889999</td>\n",
       "      <td>25809300.0</td>\n",
       "      <td>45.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>45.009998</td>\n",
       "      <td>42.430000</td>\n",
       "      <td>43.040001</td>\n",
       "      <td>43.520000</td>\n",
       "      <td>25784700.0</td>\n",
       "      <td>43.520000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2019-09-09  58.450001  57.560001  57.980000  58.110001   5740500.0  57.286106\n",
       "2019-09-10  58.720001  57.799999  58.080002  58.639999   4409600.0  57.808586\n",
       "2019-09-11  59.180000  58.139999  58.619999  58.779999   4163700.0  57.946606\n",
       "2019-09-12  59.889999  58.779999  58.779999  59.439999   5188700.0  58.597248\n",
       "2019-09-13  60.580002  59.610001  60.000000  60.009998   7548400.0  59.159164\n",
       "2019-09-16  59.130001  57.250000  58.029999  59.070000   9556400.0  58.232494\n",
       "2019-09-17  59.770000  58.160000  58.310001  59.369999   8176700.0  58.528240\n",
       "2019-09-18  59.500000  58.669998  59.450001  59.310001   4075600.0  58.469090\n",
       "2019-09-19  59.250000  58.310001  59.250000  58.689999   3569900.0  57.857876\n",
       "2019-09-20  59.180000  58.490002  58.799999  58.630001   6139400.0  57.798733\n",
       "2019-09-23  58.660000  57.840000  58.310001  58.020000   4387100.0  57.197380\n",
       "2019-09-24  58.910000  57.560001  58.619999  58.000000   5047700.0  57.177662\n",
       "2019-09-25  58.490002  57.680000  58.040001  58.310001   3193100.0  57.483269\n",
       "2019-09-26  58.810001  57.930000  58.110001  58.779999   3795400.0  57.946606\n",
       "2019-09-27  59.380001  58.160000  58.970001  58.279999   4395800.0  57.453693\n",
       "2019-09-30  58.689999  57.520000  58.509998  57.599998   5208200.0  56.783333\n",
       "2019-10-01  58.680000  56.650002  58.049999  57.009998   7102200.0  56.201698\n",
       "2019-10-02  55.750000  52.869999  55.740002  54.349998  13770400.0  53.579414\n",
       "2019-10-03  53.860001  51.810001  53.119999  52.830002  14346800.0  52.080967\n",
       "2019-10-04  53.959999  52.680000  52.849998  53.810001   6620900.0  53.047070\n",
       "2019-10-07  54.110001  53.240002  53.750000  53.360001   8659800.0  52.603451\n",
       "2019-10-08  54.099998  52.889999  53.020000  53.200001   8039400.0  52.445721\n",
       "2019-10-09  54.400002  53.590000  54.009998  53.919998   7615300.0  53.155510\n",
       "2019-10-10  53.259998  51.070000  51.950001  53.099998  22703500.0  52.347134\n",
       "2019-10-11  53.770000  52.639999  53.500000  52.950001  10935000.0  52.199261\n",
       "2019-10-14  53.200001  51.970001  52.049999  52.990002   6631900.0  52.238701\n",
       "2019-10-15  53.990002  53.009998  53.209999  53.639999   8413300.0  52.879482\n",
       "2019-10-16  54.130001  53.070000  53.750000  53.169998   5532800.0  52.416142\n",
       "2019-10-17  53.759998  53.250000  53.520000  53.369999   4402000.0  52.613308\n",
       "2019-10-18  53.990002  53.180000  53.189999  53.830002   6966400.0  53.066788\n",
       "...               ...        ...        ...        ...         ...        ...\n",
       "2020-01-27  57.180000  55.619999  56.490002  56.830002  13090200.0  56.440460\n",
       "2020-01-28  57.970001  56.750000  57.549999  57.480000   6343700.0  57.086002\n",
       "2020-01-29  57.799999  56.570000  57.639999  56.650002   5662000.0  56.261692\n",
       "2020-01-30  57.180000  55.650002  55.650002  57.099998   5980700.0  56.708603\n",
       "2020-01-31  57.080002  55.430000  56.700001  55.740002   9320800.0  55.357929\n",
       "2020-02-03  57.250000  56.000000  56.200001  56.040001   7499000.0  55.655872\n",
       "2020-02-04  58.400002  57.150002  57.200001  57.930000   6880800.0  57.532917\n",
       "2020-02-05  59.299999  58.599998  58.970001  58.889999   6067000.0  58.486336\n",
       "2020-02-06  60.029999  58.020000  59.799999  58.180000   5210200.0  57.781204\n",
       "2020-02-07  58.250000  57.520000  57.959999  57.919998   3836800.0  57.522984\n",
       "2020-02-10  58.119999  57.430000  57.540001  57.709999   4541100.0  57.314423\n",
       "2020-02-11  59.099998  57.970001  58.099998  58.660000   4566500.0  58.257912\n",
       "2020-02-12  59.919998  59.180000  59.290001  59.470001   5227500.0  59.062363\n",
       "2020-02-13  59.209999  58.349998  58.750000  59.130001   4353100.0  58.724693\n",
       "2020-02-14  59.470001  58.580002  59.240002  58.900002   3810100.0  58.496269\n",
       "2020-02-18  59.470001  58.410000  58.869999  58.720001   5690600.0  58.317501\n",
       "2020-02-19  58.580002  58.029999  58.490002  58.509998   4399900.0  58.509998\n",
       "2020-02-20  58.990002  58.119999  58.200001  58.380001   5239400.0  58.380001\n",
       "2020-02-21  58.020000  56.869999  57.959999  57.869999   6295600.0  57.869999\n",
       "2020-02-24  55.380001  53.340000  54.830002  54.230000  17983100.0  54.230000\n",
       "2020-02-25  54.709999  50.750000  54.540001  50.889999  15836500.0  50.889999\n",
       "2020-02-26  51.770000  49.000000  51.439999  49.590000  15985900.0  49.590000\n",
       "2020-02-27  50.980000  45.029999  46.720001  48.189999  21949100.0  48.189999\n",
       "2020-02-28  47.790001  45.139999  46.020000  46.130001  19861000.0  46.130001\n",
       "2020-03-02  47.250000  44.330002  46.220001  47.160000  20098700.0  47.160000\n",
       "2020-03-03  49.970001  46.040001  49.810001  46.180000  26344100.0  46.180000\n",
       "2020-03-04  48.639999  46.020000  47.500000  48.500000  20381800.0  48.500000\n",
       "2020-03-05  46.570000  44.419998  46.500000  45.009998  22918800.0  45.009998\n",
       "2020-03-06  46.930000  42.869999  43.470001  45.889999  25809300.0  45.889999\n",
       "2020-03-09  45.009998  42.430000  43.040001  43.520000  25784700.0  43.520000\n",
       "\n",
       "[126 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#By using DataReader we will get the stock quote from Yahoo Finance\n",
    "Delta_df = web.DataReader('DAL', data_source = 'yahoo', start = '2019-09-09', end = '2020-03-09')\n",
    "#Show the data\n",
    "Delta_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the number of rows and columns in the data set\n",
    "Delta_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the closing price history \n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Closing Price History')\n",
    "plt.plot(Delta_df['Close'])\n",
    "plt.xlabel('Date', fontsize=18)\n",
    "plt.ylabel('Closing Price for Delta USD ($)', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new dataframe with only the \"Close\" column\n",
    "data = Delta_df.filter(['Close'])\n",
    "#Convert the dataframe to a numpy array\n",
    "dataset = data.values\n",
    "#Get the number of rows to train the model on \n",
    "training_data_len = math.ceil( len(dataset) * .7)\n",
    "\n",
    "training_data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.78822266],\n",
       "       [0.81685577],\n",
       "       [0.82441922],\n",
       "       [0.86007561],\n",
       "       [0.89086977],\n",
       "       [0.84008647],\n",
       "       [0.85629389],\n",
       "       [0.85305253],\n",
       "       [0.81955697],\n",
       "       [0.81631561],\n",
       "       [0.78336042],\n",
       "       [0.78227989],\n",
       "       [0.79902767],\n",
       "       [0.82441922],\n",
       "       [0.79740679],\n",
       "       [0.76066987],\n",
       "       [0.7287952 ],\n",
       "       [0.58508909],\n",
       "       [0.50297149],\n",
       "       [0.55591582],\n",
       "       [0.53160459],\n",
       "       [0.52296063],\n",
       "       [0.56185838],\n",
       "       [0.51755802],\n",
       "       [0.50945441],\n",
       "       [0.51161546],\n",
       "       [0.54673149],\n",
       "       [0.52133974],\n",
       "       [0.53214475],\n",
       "       [0.55699634],\n",
       "       [0.59751499],\n",
       "       [0.60129671],\n",
       "       [0.55591582],\n",
       "       [0.5548353 ],\n",
       "       [0.5985953 ],\n",
       "       [0.62938945],\n",
       "       [0.64073483],\n",
       "       [0.63749326],\n",
       "       [0.62452741],\n",
       "       [0.6753107 ],\n",
       "       [0.72177211],\n",
       "       [0.75580762],\n",
       "       [0.75958955],\n",
       "       [0.76391144],\n",
       "       [0.75958955],\n",
       "       [0.73960021],\n",
       "       [0.74014057],\n",
       "       [0.70124261],\n",
       "       [0.70394381],\n",
       "       [0.72015123],\n",
       "       [0.73257713],\n",
       "       [0.73203676],\n",
       "       [0.65640188],\n",
       "       [0.63695311],\n",
       "       [0.68017295],\n",
       "       [0.6931388 ],\n",
       "       [0.75310642],\n",
       "       [0.73203676],\n",
       "       [0.74500282],\n",
       "       [0.71474882],\n",
       "       [0.65370068],\n",
       "       [0.67747175],\n",
       "       [0.66774726],\n",
       "       [0.68179363],\n",
       "       [0.66342517],\n",
       "       [0.64883843],\n",
       "       [0.62452741],\n",
       "       [0.71150726],\n",
       "       [0.7136683 ],\n",
       "       [0.80497024],\n",
       "       [0.81955697],\n",
       "       [0.81361441],\n",
       "       [0.8438682 ],\n",
       "       [0.83468407],\n",
       "       [0.86007561],\n",
       "       [0.85737441],\n",
       "       [0.86655875],\n",
       "       [0.84062684],\n",
       "       [0.81793629],\n",
       "       [0.8082118 ],\n",
       "       [0.83846579],\n",
       "       [0.78552146],\n",
       "       [0.76391144],\n",
       "       [0.76121023],\n",
       "       [0.82820094],\n",
       "       [0.83414371],\n",
       "       [0.84927081],\n",
       "       [0.86277702],\n",
       "       [0.96866569],\n",
       "       [0.97784982],\n",
       "       [0.9918964 ],\n",
       "       [1.        ],\n",
       "       [0.90869807],\n",
       "       [0.87088063],\n",
       "       [0.90491634],\n",
       "       [0.8260401 ],\n",
       "       [0.71907091],\n",
       "       [0.75418694],\n",
       "       [0.70934642],\n",
       "       [0.73365744],\n",
       "       [0.66018381],\n",
       "       [0.67639123],\n",
       "       [0.77849817],\n",
       "       [0.83036198],\n",
       "       [0.79200439],\n",
       "       [0.77795781],\n",
       "       [0.76661264],\n",
       "       [0.81793629],\n",
       "       [0.8616965 ],\n",
       "       [0.84332804],\n",
       "       [0.83090235],\n",
       "       [0.82117786],\n",
       "       [0.80983248],\n",
       "       [0.8028094 ],\n",
       "       [0.77525661],\n",
       "       [0.57860616],\n",
       "       [0.39816313],\n",
       "       [0.32793086],\n",
       "       [0.25229598],\n",
       "       [0.14100491],\n",
       "       [0.19665044],\n",
       "       [0.14370611],\n",
       "       [0.26904376],\n",
       "       [0.08049692],\n",
       "       [0.12803885],\n",
       "       [0.        ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scale the data with using an ue of array with numpy\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(dataset)\n",
    "\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.78822266, 0.81685577, 0.82441922, 0.86007561, 0.89086977,\n",
      "       0.84008647, 0.85629389, 0.85305253, 0.81955697, 0.81631561,\n",
      "       0.78336042, 0.78227989, 0.79902767, 0.82441922, 0.79740679,\n",
      "       0.76066987, 0.7287952 , 0.58508909, 0.50297149, 0.55591582,\n",
      "       0.53160459, 0.52296063, 0.56185838, 0.51755802, 0.50945441,\n",
      "       0.51161546, 0.54673149, 0.52133974, 0.53214475, 0.55699634,\n",
      "       0.59751499, 0.60129671, 0.55591582, 0.5548353 , 0.5985953 ,\n",
      "       0.62938945, 0.64073483, 0.63749326, 0.62452741, 0.6753107 ,\n",
      "       0.72177211, 0.75580762, 0.75958955, 0.76391144, 0.75958955,\n",
      "       0.73960021, 0.74014057, 0.70124261, 0.70394381, 0.72015123,\n",
      "       0.73257713, 0.73203676, 0.65640188, 0.63695311, 0.68017295,\n",
      "       0.6931388 , 0.75310642, 0.73203676, 0.74500282, 0.71474882])]\n",
      "[0.6537006791645585]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Creating the training data set\n",
    "#Creating the scaled training data set\n",
    "train_data = scaled_data[0:training_data_len , :]\n",
    "#Split the data into x train and y train data sets\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60, len(train_data)):\n",
    "    x_train.append(train_data[i-60:i , 0])\n",
    "    y_train.append(train_data[i, 0])\n",
    "    if i<=60: \n",
    "        print(x_train)\n",
    "        print(y_train)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convering the x_train and y_train to numpy arrays\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 60, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reshape the data\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building  the LSTM Model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compiling the model\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "29/29 [==============================] - 3s 89ms/step - loss: 0.1134\n",
      "Epoch 2/15\n",
      "29/29 [==============================] - 2s 62ms/step - loss: 0.0062\n",
      "Epoch 3/15\n",
      "29/29 [==============================] - 2s 52ms/step - loss: 0.0063\n",
      "Epoch 4/15\n",
      "29/29 [==============================] - 1s 48ms/step - loss: 0.0059\n",
      "Epoch 5/15\n",
      "29/29 [==============================] - 1s 50ms/step - loss: 0.0110\n",
      "Epoch 6/15\n",
      "29/29 [==============================] - 2s 60ms/step - loss: 0.0097\n",
      "Epoch 7/15\n",
      "29/29 [==============================] - 2s 53ms/step - loss: 0.0068\n",
      "Epoch 8/15\n",
      "29/29 [==============================] - 2s 55ms/step - loss: 0.0048\n",
      "Epoch 9/15\n",
      "29/29 [==============================] - 2s 53ms/step - loss: 0.0095\n",
      "Epoch 10/15\n",
      "29/29 [==============================] - 2s 58ms/step - loss: 0.0081: 0s - loss: 0.008\n",
      "Epoch 11/15\n",
      "29/29 [==============================] - 2s 59ms/step - loss: 0.0053\n",
      "Epoch 12/15\n",
      "29/29 [==============================] - 2s 60ms/step - loss: 0.0115\n",
      "Epoch 13/15\n",
      "29/29 [==============================] - 2s 79ms/step - loss: 0.0073\n",
      "Epoch 14/15\n",
      "29/29 [==============================] - 2s 76ms/step - loss: 0.0096\n",
      "Epoch 15/15\n",
      "29/29 [==============================] - 2s 66ms/step - loss: 0.0059\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x11453b9b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model\n",
    "model.fit(x_train,y_train, batch_size=1, epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the data set\n",
    "#Create a new array containing scaled vals from index 1543 to 2020\n",
    "test_data = scaled_data[training_data_len - 60: , :]\n",
    "#Create the data sets x_test and y_test\n",
    "x_test = []\n",
    "y_test = dataset[training_data_len:, :]\n",
    "for i in range(60, len(test_data)):\n",
    "    x_test.append(test_data[i-60:i, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert the data to a numpy array\n",
    "x_test = np.array(x_test)\n",
    "#Reshape the data\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "#Get the models predicted price values\n",
    "predictions = model.predict(x_test)\n",
    "predictions = scaler.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.094044659588788"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt( np.mean( predictions - y_test )**2 )\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the data\n",
    "train = data[:training_data_len]\n",
    "valid = data[training_data_len:]\n",
    "valid['Predictions'] = predictions\n",
    "#Visualize the data\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Model')\n",
    "plt.xlabel('Date', fontsize=18)\n",
    "plt.ylabel('Closing Price USD ($)', fontsize=18)\n",
    "plt.plot(train['Close'])\n",
    "plt.plot(valid[['Close', 'Predictions']])\n",
    "plt.legend(['Train', 'Val', 'Predictions'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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>Close</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>61.619999</td>\n",
       "      <td>59.393944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>61.880001</td>\n",
       "      <td>59.583302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>62.029999</td>\n",
       "      <td>59.793770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>60.340000</td>\n",
       "      <td>60.007889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>59.639999</td>\n",
       "      <td>60.135738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>60.270000</td>\n",
       "      <td>60.188484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>58.810001</td>\n",
       "      <td>60.226948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>56.830002</td>\n",
       "      <td>60.185841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>57.480000</td>\n",
       "      <td>60.025955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>56.650002</td>\n",
       "      <td>59.849186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>57.099998</td>\n",
       "      <td>59.630726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>55.740002</td>\n",
       "      <td>59.426544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>56.040001</td>\n",
       "      <td>59.179153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>57.930000</td>\n",
       "      <td>58.943550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>58.889999</td>\n",
       "      <td>58.809143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>58.180000</td>\n",
       "      <td>58.771511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>57.919998</td>\n",
       "      <td>58.755394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>57.709999</td>\n",
       "      <td>58.746559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>58.660000</td>\n",
       "      <td>58.734924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>59.470001</td>\n",
       "      <td>58.765743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13</th>\n",
       "      <td>59.130001</td>\n",
       "      <td>58.848743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-14</th>\n",
       "      <td>58.900002</td>\n",
       "      <td>58.936771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>58.720001</td>\n",
       "      <td>59.014862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>58.509998</td>\n",
       "      <td>59.076050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>58.380001</td>\n",
       "      <td>59.115665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>57.869999</td>\n",
       "      <td>59.136044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>54.230000</td>\n",
       "      <td>59.122360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>50.889999</td>\n",
       "      <td>58.932884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>49.590000</td>\n",
       "      <td>58.529808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>48.189999</td>\n",
       "      <td>57.987068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>46.130001</td>\n",
       "      <td>57.336205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>47.160000</td>\n",
       "      <td>56.573135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>46.180000</td>\n",
       "      <td>55.837528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>55.108250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>45.009998</td>\n",
       "      <td>54.525227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>45.889999</td>\n",
       "      <td>53.900345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>43.520000</td>\n",
       "      <td>53.334404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close  Predictions\n",
       "Date                              \n",
       "2020-01-15  61.619999    59.393944\n",
       "2020-01-16  61.880001    59.583302\n",
       "2020-01-17  62.029999    59.793770\n",
       "2020-01-21  60.340000    60.007889\n",
       "2020-01-22  59.639999    60.135738\n",
       "2020-01-23  60.270000    60.188484\n",
       "2020-01-24  58.810001    60.226948\n",
       "2020-01-27  56.830002    60.185841\n",
       "2020-01-28  57.480000    60.025955\n",
       "2020-01-29  56.650002    59.849186\n",
       "2020-01-30  57.099998    59.630726\n",
       "2020-01-31  55.740002    59.426544\n",
       "2020-02-03  56.040001    59.179153\n",
       "2020-02-04  57.930000    58.943550\n",
       "2020-02-05  58.889999    58.809143\n",
       "2020-02-06  58.180000    58.771511\n",
       "2020-02-07  57.919998    58.755394\n",
       "2020-02-10  57.709999    58.746559\n",
       "2020-02-11  58.660000    58.734924\n",
       "2020-02-12  59.470001    58.765743\n",
       "2020-02-13  59.130001    58.848743\n",
       "2020-02-14  58.900002    58.936771\n",
       "2020-02-18  58.720001    59.014862\n",
       "2020-02-19  58.509998    59.076050\n",
       "2020-02-20  58.380001    59.115665\n",
       "2020-02-21  57.869999    59.136044\n",
       "2020-02-24  54.230000    59.122360\n",
       "2020-02-25  50.889999    58.932884\n",
       "2020-02-26  49.590000    58.529808\n",
       "2020-02-27  48.189999    57.987068\n",
       "2020-02-28  46.130001    57.336205\n",
       "2020-03-02  47.160000    56.573135\n",
       "2020-03-03  46.180000    55.837528\n",
       "2020-03-04  48.500000    55.108250\n",
       "2020-03-05  45.009998    54.525227\n",
       "2020-03-06  45.889999    53.900345\n",
       "2020-03-09  43.520000    53.334404"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#showing the prediction we did and actual closing price \n",
    "valid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Linear REGRESSION MODEL FOR PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Linear Regression for Delta\n",
    "\n",
    "forecast_time = int(15) # write number of predicitons that you want to make e.g 5,10,15 etc.\n",
    "#Repalce Delta_df with dataframe of your desired company whose prediciton you want to make e.g Airchina_df, Qantas_df etc.\n",
    "LR_df=Delta_df[['Open','High','Low','Close','Adj Close','Volume']].copy()  # \n",
    "#LR_df=Delta_df.iloc[:,1:].copy()\n",
    "\n",
    "#Replace High with the column that you want to predict\n",
    "LR_df['prediction'] = LR_df['Close'].shift(-1)\n",
    "LR_df.dropna(inplace=True)\n",
    "X1 = np.array(LR_df.drop(['prediction'], 1))\n",
    "Y1 = np.array(LR_df['prediction'])\n",
    "X2 = preprocessing.scale(X1)\n",
    "X_prediction = X2[-forecast_time:]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X2, Y1, test_size=1)\n",
    "clf = LinearRegression()\n",
    "clf.fit(X_train, Y_train)\n",
    "prediction = (clf.predict(X_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from data plotting\n",
    "plt.title(\"Prediction\")\n",
    "plt.xlabel(\"Number of days\")\n",
    "\n",
    "#Replace * with the column that you want to predict\n",
    "plt.ylabel(\"*\");\n",
    "plt.plot(prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.1540349980887\n",
      "57.240160190536336\n",
      "56.74640505134568\n",
      "57.57287603582609\n",
      "56.51528736396697\n",
      "57.085693213582026\n",
      "57.621890003077034\n",
      "58.79114539779025\n",
      "57.60744173578795\n",
      "55.824063143779114\n",
      "60.97859645729671\n",
      "58.34903498382696\n",
      "57.47006643974451\n",
      "43.32403058204651\n",
      "36.28930466921944\n"
     ]
    }
   ],
   "source": [
    "for pred in prediction:\n",
    "    print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Linear Regression for Airchina\n",
    "\n",
    "forecast_time = int(15) # write number of predicitons that you want to make e.g 5,10,15 etc.\n",
    "#Repalce Delta_df with dataframe of your desired company whose prediciton you want to make e.g Airchina_df, Qantas_df etc.\n",
    "LR_df=Airchina_df[['Open','High','Low','Close','Adj Close','Volume']].copy()  # \n",
    "#LR_df=Delta_df.iloc[:,1:].copy()\n",
    "\n",
    "#Replace High with the column that you want to predict\n",
    "LR_df['prediction'] = LR_df['Close'].shift(-1)\n",
    "LR_df.dropna(inplace=True)\n",
    "X1 = np.array(LR_df.drop(['prediction'], 1))\n",
    "Y1 = np.array(LR_df['prediction'])\n",
    "X2 = preprocessing.scale(X1)\n",
    "X_prediction = X2[-forecast_time:]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X2, Y1, test_size=1)\n",
    "clf = LinearRegression()\n",
    "clf.fit(X_train, Y_train)\n",
    "prediction = (clf.predict(X_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from data plotting\n",
    "plt.title(\"Prediction\")\n",
    "plt.xlabel(\"Number of days\")\n",
    "\n",
    "#Replace * with the column that you want to predict\n",
    "plt.ylabel(\"*\");\n",
    "plt.plot(prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.981440652822137\n",
      "17.964614218904476\n",
      "19.718413381310935\n",
      "19.616151846719855\n",
      "20.090296863143486\n",
      "20.603030805447105\n",
      "19.837559952307252\n",
      "18.831710170265193\n",
      "16.325697294421218\n",
      "17.00477719639071\n",
      "18.008699400532457\n",
      "17.248106873274637\n",
      "17.58287575364804\n",
      "16.327732878036834\n",
      "15.511932456960201\n"
     ]
    }
   ],
   "source": [
    "for pred in prediction:\n",
    "    print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}