Airline Prediction_Baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import read_csv\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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opmmWEsREdHmnb39gSWYDkq6hIGXuTByOuee5VxcZi6EtfX5OdHgpzE6PjfGq\ndcVoDVrbs4GHtdD8essAG8rd8+47v5xJoBciRdoHR2J7t1rW21R5Y1wVG5w1zVKR70KpxFI3XUOB\nGRd2E5GfbWzO0dTn40TnEJsr82ILpJesKiDbbAw2n9JKS1VBFp7hIK8190t+fhYS6IVIkbaBkVhp\npWWdGejPJpmn11pPSPf0+UKEItFZA3O600GZOzOh1M1M7RSSUVuUTYPHx6lOL1vMtI01lt1mnbsd\nM3orLeUNRLhEAv2MJNALkQJaa9oHRmKLoZbczDSq8l2cTbLy5sf7WrjoM7/hBy83MuAP8ecPvgbA\nxQnkpasKsqZtbNbU66O510gjdc7QCTMZtcXZvNrYTzASjeXnLXdur2ZThXteC76WqnH1/rIQOzNJ\nagmRAv3+MIFwdErqBowrZJOtpT/Q1E8gHOUfHj3GF548RSgS5cvv3h6bIc+kKj8rtjA63lAgzF3/\n/TK5mWn88i+uwRuIzOtiKcvqomxCEePbx5RAv6OaO3dUz/s1YGxG70p3sCGBnalWMpnRC5ECrf3T\nX+a/vjSXcz3DRKOJbxje2Otj95oiPnv7VmoKs3jgg7u5Y3tiAbOqwEX74MiUDcq/8OuTdHuDnPf4\n+MWhNmB+F0tZrBLLdKeKrUmkgtVE7eLq/KQ6X65Es747SimXUmqfUup1pdQxpdRnzeNrlFJ7lVJn\nlVI/VUplmMczzdtnzfvrUvsrCLH4WNvcxctFryvLIRCOTmmzO5OmXh9rS3K496o6nvyrN3DluuKE\nn1uZn0UgHKXfH44de7Wxjwf3NnPP5bUUZKfz/z97FphfDb2l1iyxXFeaS0Za6gKwK93JJTX5XG82\nMBPTS+S/QhC4Xmt9CbAduFkpdQXwBeBLWuv1QD/wIfPxHwL6zeNfMh8nxIpizejj9XNZW2LMcs/3\nJNbczBsI4xkOsbo4Z05jideX/jOPHaO6IIv/fetm3rWzJpbDtyPQW+PcMiltkwqPfvQa/vy6dSl/\nnaVu1kCvDVZCMd38o4HrgYfN4w8Ad5o/32Hexrz/BiUNKMQitL+xjz5z6zm7tfT7yc9KJ881tc3v\nulIjEJ7vSSxP32Qulq4pmVulSp3Z6Ou82TVzOBjhWPsQ79m9ipzMNN57eW3ssXYsxlbkudhZW8Cb\nt8hMe7FI6HuVUsqplDoEdANPAeeAAa211UCjFbAShtVAC4B5/yCQ+PdMIRZAeDTKe7+1lz/74YGk\ncuWJau0fmbafS6k7k5wMZ8LtihvNbpBzndFbzdTOdhklnVavnfpyNwBrS3O5en0x7sw03HE+mJLl\ndCge+fDV3HJx5bzPJeyRUKAiXFz5AAAgAElEQVTXWo9qrbcDNcBuILG9xWaglLpPKbVfKbW/p6dn\nvqcTIinNfX5CkSj7Gvp46ECL7edv6fNTUxB/Bq6UYm1pbmyGPZtGjxXo5zajz0xzsro4h7PmNwir\n4mf8Qunn3n4x//neHXM6v1j8klop0VoPAM8BVwIFSimrPLMGaDN/bgNWAZj35wO9cc71Da31Lq31\nrtLS0jkOX4i5aTDz45X5Lv7lVyfo8c68z2kytNYzzugB1pbmJJyjb+z1U56XSXbG3Kuh15flxq7G\nPdPtJd2pWD1uoXh1cQ7XJbABuFiaEqm6KVVKFZg/ZwFvBk5gBPx3mQ+7F3jU/Pkx8zbm/c/qyXVd\nQlxg5z1G0PvqPTsJhKN88anTtp3bMxwiGIlSM0OHxrUlubQNjDASmn1bwaZe35zTNpb1Zbk0eHyE\nR6Oc6x5mbUmulCSuIIn8l64EnlNKHQZeBZ7SWj8OfBL4uFLqLEYO/tvm478NFJvHPw58yv5hCzE/\nDR4fxTkZ7Kwt5A0bStnfaN+m3daeqLPN6K1xzKbB42fNPAN9fVkukaimqdfHme5h1ssFRivKrN8F\ntdaHgSnJO631eYx8/eTjAeAuW0YnRIqc7/HFtp3bUJ7L86e6CUWittR9t/YbZYwzzuityhvPMFuq\npi9DHA5G8AwHWT3HihtLfZmx8Hq0bYjmPj93JnixlVge5LubWJHOe8YCfX25Mdtt7LVn025r8+vq\nOO0PLNZrT5enf+p4F4daBmILsfOd0a8rM57/2+OdaG38zmLlkF43YsXxBsL0eIOsLTWCnTXbPd3l\nZYNZcjgfrf0jFOdkzNgfPTvDaG4Wr5b+J/ua+dQjR8jNTONPrl0LzL20cvzrVRdk8dxJo8LN+p3F\nyiAzerHiNHqsC5CM4Lm+LBeHgtPz7BFvae33U5NAv/V4JZaPvd7O/T8/wtXri3GlO/jS08Yi8VxL\nK8erL89lJDyKQ0HdPFNBYmmRQC9WHKvixrpC1dq0O9ke8dNp7R+hJoF+62tKcmjo8cWajQUjo/zt\nw6+za3Uh33rfZXztDy8l3akoc2fasnvSevMbTF1xzoRdr8TyJ6kbseKc7/Gh1FjzLTCuErVjRh+N\natr6R7hp6+yX/68tzcEbjNAzHKTM7aKlb4RAOMo9l68mK8PJZXVFfPW9OxkY14xsPqy8fCo7SorF\nSWb0YlEKRkb5w2/t5Tt7Gmw/93mPj5rCrAmz2g3luTR6fLE+6nPVPjhCaDTKqhkqbizWGoG1INvU\nO/UK2Ju2VnD3ZavmNSbLejMvLwuxK4/M6MWi9K9PnmLPWQ9RrfngNWtsPXeDZzjWQdKyodxNJKpp\n8PjYWJH4QqXWmqNtQzx6qI3nTnXH6uLrElg8XTuu8uaKtcU0ms3L5rvwOp3NlW4uqcmXK2BXIAn0\nYtF54XQP397TQEaaI+kt92ajtaahx8eu1RN3ZrLSGae7vEkF+m/vaeCff3WCdKfi6vUl3HpxJRdV\n5yfUL766IIvMNEes8qa514fblUZh9vwbi8WTnZHGox+9JiXnFoubBHqxqATCo/zNQ69TX5bLW7dV\n8aWnTzPoD5NvU/Dr9gbxhUZjFyxZ1pUalTdnupJbkP3d6R7qy3J5+M+uSnqMDodiTUlOrPKmsdfP\n6uJspKu3sJvk6MWisr+xn25vkE/evImLqo0rRs/22FMNA8T2Tp1cL+9KNzo8Jrsge7rLy8U1+XP+\nIDKam5kz+j5/ytI2YmWTQC8WlRfP9pDuVFy5rjh2UY+d6Ztj7Uagj9d2oL4sl9NJzOgHR8J0DQXn\ndfHR2pJcWvpHCIRHaenzT+goKYRdJNCLReXF0x521haSk5lGdaGRwz5j04VMAMc7hlhVlBV356dN\nlXk09PrwhyJxnjmVVXe/YR5VLGtLcxiNal4+10skqhNaxBUiWRLoxaLhGQ5yvGOIa+tLAGOnonWl\nubGNMuxwvH2IrZX5ce/bWpWH1nCyM7FZvZXmmU/bBKvE8rlT3YA9V8AKMZkEerFovHTWA8C19WMb\n0dSX59qWuhkORmjw+Ng6TbdI67iV3pnN6S4vWenOGZuXzcZqw/DsSSvQy4xe2E8CvVg0XjzjIT8r\nnYuqx2bc60uNDTp8wcTSKTM52TF9fh6Mcsf8rHSOJxjoz3QNU1+ei8Mx9yqZ/Kx0SnIzaO0fwZXu\noMw9/825hZhMAr1YFLTW7Dnj4er1xTjHBU7rKs5zcbo8JsuaqW+tip+6UUqxpTKP4+2DCZ3vdJfX\nli6Q1sVbtUXZ8/rQEGI6EuhF0p482knbwIit5zzdNUznUGBC2gbGLtu3I31zrH2QopwMyvOmnzVv\nrcrjZKeXyGj8VggvnO6hpc/PoD9MtzdoSzsBq6Zf0jYiVSTQi6Sc6Bjiz354gK//7pyt531ofwtp\nDsUNmydenr+6OJs0h7JlQfZ4xxBbq/JmvCBpS1UewUh0SvtgMNoPv/+7+7j3u/s4as7651NxY4kF\neimtFCkigV4k5WvPGwH+VIKVKZN5A2Fa+vxEo2P7xQfCozz8Witv2VpBmds14fHpTgdrSnLmXWIZ\nHo1yunPmbftgLK1zLE765vsvN6ExetP8w6NHAXs28LBSN6tLZEYvUkNaIIiENXp8PH64nXSn4nSX\nF6110pfrf/RHB/nd6R5yMpy8cWMp/37Xdn59tIMBf5h7Lq+N+5yNFW5ea+pP6nV+sq+Z4x1DfPb2\nrSilONM1TGg0ypbKmQP9utIcMtIcHG8f4u3jdkr2BSP8eF8zt11cidOhePRQO9kZ86u4sexcXci2\nmnyuSqA/jhBzIYFeJOzrL5wnzengj69Zw389f44eb5CyPNfsTxzneMcQO2sL2FyZx4/3NeMN7Gco\nEGFtSc60jcB21Bby+OEOuoYClCf4er841MYr5/uoL3dzz+5avvjUaZwOxc7awhmfl+Z0sKnCPaXE\n8uEDrXgDET54zRpqi7J5/lQPa0pybFk8LcrJ4DFpNiZSSAK9SEjXUICfHWjlrl01XFNfwn89f45T\nXd6kAr0vGKHHG+T9V9XxkTet55JVBfztw4cB+PvbNk/77WBHbQEAB5sHuPmiioRey1os/ufHj/N6\nywBPn+jiM2/bwqoE8uBbq/L49dHO2DeWaFTz3Zca2L6qIPZB8eAfXz6hOkiIxUxy9CIh33rxPJFo\nlD99wzo2mleCJpunb+yd2Kv97l2r+D93XsSWyjzeubNm2udtrcojw+ngYHNi6ZvRqKZjIMC7Lq0h\nJzONhw+0cveuGu69qi6h52+tymfAH6alz/iwONnppbHXz3vHpZYuqs5n8yxpICEWC5nRi1n1+0I8\nuLeZ2y+pim2/V5KbmVQDMIAmc2ON8RtT/9EVq/mjK1bP+LzMNCdbqvI42DyQ0Ot0ewNEopodtQXc\ndWkNvz7ayf23bkp4PSH2DaKln9ribA62GB8wl68pmulpQixaMqMXs3rg5Ub8oVH+/Lr1sWMbK3I5\nlWQljLX70lzqxXfUFnC4bYD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lpDkUz5zo4mBzP2XuTKrypfRRCJEay6bXzY/3taCB9+42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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bcdbbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the dataset\n",
    "dataframe = read_csv('international-airline-passengers.csv', usecols=[1], engine='python', skipfooter=3)\n",
    "dataset = dataframe.values\n",
    "# to float\n",
    "dataset = dataset.astype('float32')\n",
    "\n",
    "plt.plot(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 118.,  132.], dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[1:3,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return numpy.array(dataX), numpy.array(dataY)\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('international-airline-passengers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>Month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1949-06</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1949-07</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1949-08</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1949-09</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1949-10</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1949-11</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1949-12</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1950-01</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1950-02</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1950-03</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1950-04</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1950-05</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1950-06</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1950-07</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950-08</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1950-09</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1950-10</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1950-11</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1950-12</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1951-01</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1951-02</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1951-03</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1951-04</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1951-05</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1951-06</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1958-07</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1958-08</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1958-09</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1958-10</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1958-11</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1958-12</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1959-01</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1959-02</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1959-03</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1959-04</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1959-05</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1959-06</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1959-07</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1959-08</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1959-09</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1959-10</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1959-11</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1959-12</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1960-01</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1960-02</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1960-03</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1960-04</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1960-05</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1960-06</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1960-07</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  passengers\n",
       "0    1949-01         112\n",
       "1    1949-02         118\n",
       "2    1949-03         132\n",
       "3    1949-04         129\n",
       "4    1949-05         121\n",
       "5    1949-06         135\n",
       "6    1949-07         148\n",
       "7    1949-08         148\n",
       "8    1949-09         136\n",
       "9    1949-10         119\n",
       "10   1949-11         104\n",
       "11   1949-12         118\n",
       "12   1950-01         115\n",
       "13   1950-02         126\n",
       "14   1950-03         141\n",
       "15   1950-04         135\n",
       "16   1950-05         125\n",
       "17   1950-06         149\n",
       "18   1950-07         170\n",
       "19   1950-08         170\n",
       "20   1950-09         158\n",
       "21   1950-10         133\n",
       "22   1950-11         114\n",
       "23   1950-12         140\n",
       "24   1951-01         145\n",
       "25   1951-02         150\n",
       "26   1951-03         178\n",
       "27   1951-04         163\n",
       "28   1951-05         172\n",
       "29   1951-06         178\n",
       "..       ...         ...\n",
       "114  1958-07         491\n",
       "115  1958-08         505\n",
       "116  1958-09         404\n",
       "117  1958-10         359\n",
       "118  1958-11         310\n",
       "119  1958-12         337\n",
       "120  1959-01         360\n",
       "121  1959-02         342\n",
       "122  1959-03         406\n",
       "123  1959-04         396\n",
       "124  1959-05         420\n",
       "125  1959-06         472\n",
       "126  1959-07         548\n",
       "127  1959-08         559\n",
       "128  1959-09         463\n",
       "129  1959-10         407\n",
       "130  1959-11         362\n",
       "131  1959-12         405\n",
       "132  1960-01         417\n",
       "133  1960-02         391\n",
       "134  1960-03         419\n",
       "135  1960-04         461\n",
       "136  1960-05         472\n",
       "137  1960-06         535\n",
       "138  1960-07         622\n",
       "139  1960-08         606\n",
       "140  1960-09         508\n",
       "141  1960-10         461\n",
       "142  1960-11         390\n",
       "143  1960-12         432\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.67)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this function to prepare the train and test datasets for modeling\n",
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = numpy.reshape(trainX, (trainX.shape[0], look_back, trainX.shape[1]))\n",
    "testX = numpy.reshape(testX, (testX.shape[0], look_back, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.0340\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0149\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0107\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0097\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0089\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0080\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0055\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0048\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0029\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0019\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0019\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0020\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c1e158ad0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "model = Sequential()\n",
    "model.add(LSTM(4, 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=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform([testY])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 22.76 RMSE\n",
      "Test Score: 45.86 RMSE\n"
     ]
    }
   ],
   "source": [
    "trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kUqK5OWvpY6YRm9dTpCIrkxYN86W70cPRaJZESpmpjWZGl10HqbnQQq/RnOb0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c21661810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = numpy.empty_like(dataset)\n",
    "trainPredictPlot[:, :] = numpy.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = numpy.empty_like(dataset)\n",
    "testPredictPlot[:, :] = numpy.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WEq/xeTtZPlXeZAeNR1/c2MuoRbqWJm48rRrQQuc+cToXeDX0QohoIUSs/TFw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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2192ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l1,=plt.plot(scaler.inverse_transform(dataset)[-40:])\n",
    "#plt.plot(trainPredictPlot[-40:]\n",
    "l2,=plt.plot(testPredictPlot[-40:])\n",
    "plt.legend(handles = [l1, l2,], labels = ['Reality', 'Prediction'], loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}