Multivarate (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.51\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(3.51,copy=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "# rest of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Apratim\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "import csv\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Apratim\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "content=pd.read_csv('household_power_consumption.txt',delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "      <th>IS_HOLIDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "\n",
       "     T_2    T_3    T_4  T_5  IS_HOLIDAY  \n",
       "0  0.000  0.000  0.000  0.0           0  \n",
       "1  0.000  0.000  0.000  0.0           0  \n",
       "2  4.216  0.000  0.000  0.0           0  \n",
       "3  5.360  4.216  0.000  0.0           0  \n",
       "4  5.374  5.360  4.216  0.0           0  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075259"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                      object\n",
       "Time                      object\n",
       "Global_active_power       object\n",
       "Global_reactive_power     object\n",
       "Voltage                   object\n",
       "Global_intensity          object\n",
       "Sub_metering_1            object\n",
       "Sub_metering_2            object\n",
       "Sub_metering_3           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Apratim\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "content.datatime = content.Date+' '+content.Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Time', 'Global_active_power', 'Global_reactive_power',\n",
       "       'Voltage', 'Global_intensity', 'Sub_metering_1', 'Sub_metering_2',\n",
       "       'Sub_metering_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sub_metering_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.049280e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.458447e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.437154e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.100000e+01</td>\n",
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      ],
      "text/plain": [
       "       Sub_metering_3\n",
       "count    2.049280e+06\n",
       "mean     6.458447e+00\n",
       "std      8.437154e+00\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      1.000000e+00\n",
       "75%      1.700000e+01\n",
       "max      3.100000e+01"
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                     False\n",
       "Time                     False\n",
       "Global_active_power      False\n",
       "Global_reactive_power    False\n",
       "Voltage                  False\n",
       "Global_intensity         False\n",
       "Sub_metering_1           False\n",
       "Sub_metering_2           False\n",
       "Sub_metering_3            True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "content[\"DATE_TIME\"] = pd.to_datetime(content.Date + \" \" + content[\"Time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Global_active_power</th>\n",
       "      <th>Global_reactive_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>Sub_metering_1</th>\n",
       "      <th>Sub_metering_2</th>\n",
       "      <th>Sub_metering_3</th>\n",
       "      <th>DATE_TIME</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:24:00</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.418</td>\n",
       "      <td>234.840</td>\n",
       "      <td>18.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:25:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>0.436</td>\n",
       "      <td>233.630</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:26:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>0.498</td>\n",
       "      <td>233.290</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>2006-12-16 17:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:27:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>0.502</td>\n",
       "      <td>233.740</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:28:00</td>\n",
       "      <td>3.666</td>\n",
       "      <td>0.528</td>\n",
       "      <td>235.680</td>\n",
       "      <td>15.800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date      Time Global_active_power Global_reactive_power  Voltage  \\\n",
       "0  16/12/2006  17:24:00               4.216                 0.418  234.840   \n",
       "1  16/12/2006  17:25:00               5.360                 0.436  233.630   \n",
       "2  16/12/2006  17:26:00               5.374                 0.498  233.290   \n",
       "3  16/12/2006  17:27:00               5.388                 0.502  233.740   \n",
       "4  16/12/2006  17:28:00               3.666                 0.528  235.680   \n",
       "\n",
       "  Global_intensity Sub_metering_1 Sub_metering_2  Sub_metering_3  \\\n",
       "0           18.400          0.000          1.000            17.0   \n",
       "1           23.000          0.000          1.000            16.0   \n",
       "2           23.000          0.000          2.000            17.0   \n",
       "3           23.000          0.000          1.000            17.0   \n",
       "4           15.800          0.000          1.000            17.0   \n",
       "\n",
       "            DATE_TIME  \n",
       "0 2006-12-16 17:24:00  \n",
       "1 2006-12-16 17:25:00  \n",
       "2 2006-12-16 17:26:00  \n",
       "3 2006-12-16 17:27:00  \n",
       "4 2006-12-16 17:28:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.drop(['Date','Time','Global_reactive_power'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <th>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>Sub_metering_1</th>\n",
       "      <th>Sub_metering_2</th>\n",
       "      <th>Sub_metering_3</th>\n",
       "      <th>DATE_TIME</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.840</td>\n",
       "      <td>18.400</td>\n",
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       "      <td>2006-12-16 17:24:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.630</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>2006-12-16 17:25:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.290</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.740</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.680</td>\n",
       "      <td>15.800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Global_active_power  Voltage Global_intensity Sub_metering_1 Sub_metering_2  \\\n",
       "0               4.216  234.840           18.400          0.000          1.000   \n",
       "1               5.360  233.630           23.000          0.000          1.000   \n",
       "2               5.374  233.290           23.000          0.000          2.000   \n",
       "3               5.388  233.740           23.000          0.000          1.000   \n",
       "4               3.666  235.680           15.800          0.000          1.000   \n",
       "\n",
       "   Sub_metering_3           DATE_TIME  \n",
       "0            17.0 2006-12-16 17:24:00  \n",
       "1            16.0 2006-12-16 17:25:00  \n",
       "2            17.0 2006-12-16 17:26:00  \n",
       "3            17.0 2006-12-16 17:27:00  \n",
       "4            17.0 2006-12-16 17:28:00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.drop(['Sub_metering_1','Sub_metering_2','Sub_metering_3'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>DATE_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.840</td>\n",
       "      <td>18.400</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.630</td>\n",
       "      <td>23.000</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.290</td>\n",
       "      <td>23.000</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.740</td>\n",
       "      <td>23.000</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.680</td>\n",
       "      <td>15.800</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Global_active_power  Voltage Global_intensity           DATE_TIME\n",
       "0               4.216  234.840           18.400 2006-12-16 17:24:00\n",
       "1               5.360  233.630           23.000 2006-12-16 17:25:00\n",
       "2               5.374  233.290           23.000 2006-12-16 17:26:00\n",
       "3               5.388  233.740           23.000 2006-12-16 17:27:00\n",
       "4               3.666  235.680           15.800 2006-12-16 17:28:00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power            object\n",
       "Voltage                        object\n",
       "Global_intensity               object\n",
       "DATE_TIME              datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    0\n",
       "Voltage                0\n",
       "Global_intensity       0\n",
       "DATE_TIME              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075259"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.Voltage = pd.to_numeric(content.Voltage,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power        0\n",
       "Voltage                25979\n",
       "Global_intensity           0\n",
       "DATE_TIME                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2049280"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.Global_intensity = pd.to_numeric(content.Global_intensity,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    0\n",
       "Voltage                0\n",
       "Global_intensity       0\n",
       "DATE_TIME              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.Global_active_power = pd.to_numeric(content.Global_active_power,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    0\n",
       "Voltage                0\n",
       "Global_intensity       0\n",
       "DATE_TIME              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power           float64\n",
       "Voltage                       float64\n",
       "Global_intensity              float64\n",
       "DATE_TIME              datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
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       "      <th>DATE_TIME</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00\n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00\n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00\n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00\n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for obs in range(1,6):\n",
    "    content[\"T_\" + str(obs)] = content.Global_active_power.shift(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
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       "      <td>4.216</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
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       "      <td>4.216</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00    NaN   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "\n",
       "     T_2    T_3    T_4  T_5  \n",
       "0    NaN    NaN    NaN  NaN  \n",
       "1    NaN    NaN    NaN  NaN  \n",
       "2  4.216    NaN    NaN  NaN  \n",
       "3  5.360  4.216    NaN  NaN  \n",
       "4  5.374  5.360  4.216  NaN  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.520</td>\n",
       "      <td>235.02</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2006-12-16 17:29:00</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.702</td>\n",
       "      <td>235.09</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:30:00</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.700</td>\n",
       "      <td>235.22</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:31:00</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.668</td>\n",
       "      <td>233.99</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:32:00</td>\n",
       "      <td>3.700</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.662</td>\n",
       "      <td>233.86</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:33:00</td>\n",
       "      <td>3.668</td>\n",
       "      <td>3.700</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "5                3.520   235.02              15.0 2006-12-16 17:29:00  3.666   \n",
       "6                3.702   235.09              15.8 2006-12-16 17:30:00  3.520   \n",
       "7                3.700   235.22              15.8 2006-12-16 17:31:00  3.702   \n",
       "8                3.668   233.99              15.8 2006-12-16 17:32:00  3.700   \n",
       "9                3.662   233.86              15.8 2006-12-16 17:33:00  3.668   \n",
       "\n",
       "     T_2    T_3    T_4    T_5  \n",
       "0  0.000  0.000  0.000  0.000  \n",
       "1  0.000  0.000  0.000  0.000  \n",
       "2  4.216  0.000  0.000  0.000  \n",
       "3  5.360  4.216  0.000  0.000  \n",
       "4  5.374  5.360  4.216  0.000  \n",
       "5  5.388  5.374  5.360  4.216  \n",
       "6  3.666  5.388  5.374  5.360  \n",
       "7  3.520  3.666  5.388  5.374  \n",
       "8  3.702  3.520  3.666  5.388  \n",
       "9  3.700  3.702  3.520  3.666  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.fillna(0.00,inplace=True)\n",
    "content.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# content[\"V_\" + str(1)] = content.Voltage.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# content[\"Global_intensity\" + str(1)] = content.Global_intensity.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.520</td>\n",
       "      <td>235.02</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2006-12-16 17:29:00</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.702</td>\n",
       "      <td>235.09</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:30:00</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.700</td>\n",
       "      <td>235.22</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:31:00</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.668</td>\n",
       "      <td>233.99</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:32:00</td>\n",
       "      <td>3.700</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.662</td>\n",
       "      <td>233.86</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:33:00</td>\n",
       "      <td>3.668</td>\n",
       "      <td>3.700</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "5                3.520   235.02              15.0 2006-12-16 17:29:00  3.666   \n",
       "6                3.702   235.09              15.8 2006-12-16 17:30:00  3.520   \n",
       "7                3.700   235.22              15.8 2006-12-16 17:31:00  3.702   \n",
       "8                3.668   233.99              15.8 2006-12-16 17:32:00  3.700   \n",
       "9                3.662   233.86              15.8 2006-12-16 17:33:00  3.668   \n",
       "\n",
       "     T_2    T_3    T_4    T_5  \n",
       "0  0.000  0.000  0.000  0.000  \n",
       "1  0.000  0.000  0.000  0.000  \n",
       "2  4.216  0.000  0.000  0.000  \n",
       "3  5.360  4.216  0.000  0.000  \n",
       "4  5.374  5.360  4.216  0.000  \n",
       "5  5.388  5.374  5.360  4.216  \n",
       "6  3.666  5.388  5.374  5.360  \n",
       "7  3.520  3.666  5.388  5.374  \n",
       "8  3.702  3.520  3.666  5.388  \n",
       "9  3.700  3.702  3.520  3.666  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.520</td>\n",
       "      <td>235.02</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2006-12-16 17:29:00</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.702</td>\n",
       "      <td>235.09</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:30:00</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.700</td>\n",
       "      <td>235.22</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:31:00</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.668</td>\n",
       "      <td>233.99</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:32:00</td>\n",
       "      <td>3.700</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "      <td>5.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.662</td>\n",
       "      <td>233.86</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:33:00</td>\n",
       "      <td>3.668</td>\n",
       "      <td>3.700</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "5                3.520   235.02              15.0 2006-12-16 17:29:00  3.666   \n",
       "6                3.702   235.09              15.8 2006-12-16 17:30:00  3.520   \n",
       "7                3.700   235.22              15.8 2006-12-16 17:31:00  3.702   \n",
       "8                3.668   233.99              15.8 2006-12-16 17:32:00  3.700   \n",
       "9                3.662   233.86              15.8 2006-12-16 17:33:00  3.668   \n",
       "\n",
       "     T_2    T_3    T_4    T_5  \n",
       "0  0.000  0.000  0.000  0.000  \n",
       "1  0.000  0.000  0.000  0.000  \n",
       "2  4.216  0.000  0.000  0.000  \n",
       "3  5.360  4.216  0.000  0.000  \n",
       "4  5.374  5.360  4.216  0.000  \n",
       "5  5.388  5.374  5.360  4.216  \n",
       "6  3.666  5.388  5.374  5.360  \n",
       "7  3.520  3.666  5.388  5.374  \n",
       "8  3.702  3.520  3.666  5.388  \n",
       "9  3.700  3.702  3.520  3.666  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.fillna(0.00,inplace=True)\n",
    "content.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# content = content.drop(['Voltage','Global_intensity'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2049280\n",
      "Global_active_power           float64\n",
      "Voltage                       float64\n",
      "Global_intensity              float64\n",
      "DATE_TIME              datetime64[ns]\n",
      "T_1                           float64\n",
      "T_2                           float64\n",
      "T_3                           float64\n",
      "T_4                           float64\n",
      "T_5                           float64\n",
      "dtype: object\n",
      "       Global_active_power       Voltage  Global_intensity           T_1  \\\n",
      "count         2.049280e+06  2.049280e+06      2.049280e+06  2.049280e+06   \n",
      "mean          1.091615e+00  2.408399e+02      4.627759e+00  1.091615e+00   \n",
      "std           1.057294e+00  3.239987e+00      4.444396e+00  1.057294e+00   \n",
      "min           7.600000e-02  2.232000e+02      2.000000e-01  0.000000e+00   \n",
      "25%           3.080000e-01  2.389900e+02      1.400000e+00  3.080000e-01   \n",
      "50%           6.020000e-01  2.410100e+02      2.600000e+00  6.020000e-01   \n",
      "75%           1.528000e+00  2.428900e+02      6.400000e+00  1.528000e+00   \n",
      "max           1.112200e+01  2.541500e+02      4.840000e+01  1.112200e+01   \n",
      "\n",
      "                T_2           T_3           T_4           T_5  \n",
      "count  2.049280e+06  2.049280e+06  2.049280e+06  2.049280e+06  \n",
      "mean   1.091614e+00  1.091614e+00  1.091613e+00  1.091613e+00  \n",
      "std    1.057295e+00  1.057295e+00  1.057295e+00  1.057296e+00  \n",
      "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
      "25%    3.080000e-01  3.080000e-01  3.080000e-01  3.080000e-01  \n",
      "50%    6.020000e-01  6.020000e-01  6.020000e-01  6.020000e-01  \n",
      "75%    1.528000e+00  1.528000e+00  1.528000e+00  1.528000e+00  \n",
      "max    1.112200e+01  1.112200e+01  1.112200e+01  1.112200e+01  \n"
     ]
    }
   ],
   "source": [
    "print(len(content))\n",
    "print(content.dtypes)\n",
    "print(content.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Global_active_power', 'Voltage', 'Global_intensity', 'DATE_TIME',\n",
       "       'T_1', 'T_2', 'T_3', 'T_4', 'T_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
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       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
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       "    <tr>\n",
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       "      <td>5.360</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
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       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "\n",
       "     T_2    T_3    T_4  T_5  \n",
       "0  0.000  0.000  0.000  0.0  \n",
       "1  0.000  0.000  0.000  0.0  \n",
       "2  4.216  0.000  0.000  0.0  \n",
       "3  5.360  4.216  0.000  0.0  \n",
       "4  5.374  5.360  4.216  0.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2075254</th>\n",
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       "      <td>2010-11-26 20:58:00</td>\n",
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       "    <tr>\n",
       "      <th>2075256</th>\n",
       "      <td>0.938</td>\n",
       "      <td>239.82</td>\n",
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       "    <tr>\n",
       "      <th>2075257</th>\n",
       "      <td>0.934</td>\n",
       "      <td>239.70</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2010-11-26 21:01:00</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075258</th>\n",
       "      <td>0.932</td>\n",
       "      <td>239.55</td>\n",
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       "      <td>2010-11-26 21:02:00</td>\n",
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       "      <td>0.938</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Global_active_power  Voltage  Global_intensity           DATE_TIME  \\\n",
       "2075254                0.946   240.43               4.0 2010-11-26 20:58:00   \n",
       "2075255                0.944   240.00               4.0 2010-11-26 20:59:00   \n",
       "2075256                0.938   239.82               3.8 2010-11-26 21:00:00   \n",
       "2075257                0.934   239.70               3.8 2010-11-26 21:01:00   \n",
       "2075258                0.932   239.55               3.8 2010-11-26 21:02:00   \n",
       "\n",
       "           T_1    T_2    T_3    T_4    T_5  \n",
       "2075254  0.946  0.942  0.946  0.946  0.944  \n",
       "2075255  0.946  0.946  0.942  0.946  0.946  \n",
       "2075256  0.944  0.946  0.946  0.942  0.946  \n",
       "2075257  0.938  0.944  0.946  0.946  0.942  \n",
       "2075258  0.934  0.938  0.944  0.946  0.946  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal = calendar()\n",
    "holidays = cal.holidays(start = content.DATE_TIME.min(), end = content.DATE_TIME.max())\n",
    "content[\"IS_HOLIDAY\"] = content.DATE_TIME.isin(holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>DATE_TIME</th>\n",
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       "      <th>T_3</th>\n",
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       "      <th>T_5</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "\n",
       "     T_2    T_3    T_4  T_5  IS_HOLIDAY  \n",
       "0  0.000  0.000  0.000  0.0       False  \n",
       "1  0.000  0.000  0.000  0.0       False  \n",
       "2  4.216  0.000  0.000  0.0       False  \n",
       "3  5.360  4.216  0.000  0.0       False  \n",
       "4  5.374  5.360  4.216  0.0       False  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.IS_HOLIDAY = content.IS_HOLIDAY.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "      <th>IS_HOLIDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:25:00</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:27:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:28:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>5.374</td>\n",
       "      <td>5.360</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power  Voltage  Global_intensity           DATE_TIME    T_1  \\\n",
       "0                4.216   234.84              18.4 2006-12-16 17:24:00  0.000   \n",
       "1                5.360   233.63              23.0 2006-12-16 17:25:00  4.216   \n",
       "2                5.374   233.29              23.0 2006-12-16 17:26:00  5.360   \n",
       "3                5.388   233.74              23.0 2006-12-16 17:27:00  5.374   \n",
       "4                3.666   235.68              15.8 2006-12-16 17:28:00  5.388   \n",
       "\n",
       "     T_2    T_3    T_4  T_5  IS_HOLIDAY  \n",
       "0  0.000  0.000  0.000  0.0           0  \n",
       "1  0.000  0.000  0.000  0.0           0  \n",
       "2  4.216  0.000  0.000  0.0           0  \n",
       "3  5.360  4.216  0.000  0.0           0  \n",
       "4  5.374  5.360  4.216  0.0           0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=result.drop(['DATE_TIME'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    float64\n",
       "Voltage                float64\n",
       "Global_intensity       float64\n",
       "T_1                    float64\n",
       "T_2                    float64\n",
       "T_3                    float64\n",
       "T_4                    float64\n",
       "T_5                    float64\n",
       "IS_HOLIDAY               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2049280"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = int(round(0.9 * result.shape[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1844352"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = result.iloc[:row,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1844352"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "train = shuffle(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Global_active_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "      <th>IS_HOLIDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4794</th>\n",
       "      <td>0.210</td>\n",
       "      <td>246.68</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782331</th>\n",
       "      <td>1.348</td>\n",
       "      <td>236.36</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.306</td>\n",
       "      <td>1.256</td>\n",
       "      <td>1.256</td>\n",
       "      <td>1.264</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407334</th>\n",
       "      <td>0.708</td>\n",
       "      <td>238.35</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709060</th>\n",
       "      <td>0.354</td>\n",
       "      <td>243.07</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.698</td>\n",
       "      <td>1.004</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957163</th>\n",
       "      <td>1.282</td>\n",
       "      <td>237.99</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.272</td>\n",
       "      <td>1.268</td>\n",
       "      <td>1.278</td>\n",
       "      <td>1.276</td>\n",
       "      <td>1.284</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Global_active_power  Voltage  Global_intensity    T_1    T_2    T_3  \\\n",
       "4794                   0.210   246.68               0.8  0.228  0.268  0.298   \n",
       "1782331                1.348   236.36               5.6  1.340  1.306  1.256   \n",
       "407334                 0.708   238.35               3.0  0.728  0.708  0.728   \n",
       "709060                 0.354   243.07               1.6  0.350  0.372  0.698   \n",
       "957163                 1.282   237.99               5.4  1.272  1.268  1.278   \n",
       "\n",
       "           T_4    T_5  IS_HOLIDAY  \n",
       "4794     0.280  0.298           0  \n",
       "1782331  1.256  1.264           0  \n",
       "407334   0.706  0.724           0  \n",
       "709060   1.004  0.984           0  \n",
       "957163   1.276  1.284           0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    float64\n",
       "Voltage                float64\n",
       "Global_intensity       float64\n",
       "T_1                    float64\n",
       "T_2                    float64\n",
       "T_3                    float64\n",
       "T_4                    float64\n",
       "T_5                    float64\n",
       "IS_HOLIDAY               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.sample(frac=0.75, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.iloc[:, 1:]\n",
    "y_train = train.iloc[:, 0]\n",
    "X_test = result.iloc[row:, 1:]\n",
    "y_test = result.iloc[row:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "      <th>IS_HOLIDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1216481</th>\n",
       "      <td>243.32</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.356</td>\n",
       "      <td>1.356</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.472</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661539</th>\n",
       "      <td>241.50</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.404</td>\n",
       "      <td>1.426</td>\n",
       "      <td>1.420</td>\n",
       "      <td>1.424</td>\n",
       "      <td>1.422</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469987</th>\n",
       "      <td>241.55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216547</th>\n",
       "      <td>235.39</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32329</th>\n",
       "      <td>241.40</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.310</td>\n",
       "      <td>1.294</td>\n",
       "      <td>1.308</td>\n",
       "      <td>1.284</td>\n",
       "      <td>1.304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Voltage  Global_intensity    T_1    T_2    T_3    T_4    T_5  \\\n",
       "1216481   243.32               5.6  1.356  1.356  1.370  1.380  1.472   \n",
       "1661539   241.50               5.8  1.404  1.426  1.420  1.424  1.422   \n",
       "1469987   241.55               2.0  0.474  0.472  0.474  0.472  0.474   \n",
       "216547    235.39               1.6  0.368  0.368  0.356  0.280  0.210   \n",
       "32329     241.40               5.2  1.310  1.294  1.308  1.284  1.304   \n",
       "\n",
       "         IS_HOLIDAY  \n",
       "1216481           0  \n",
       "1661539           0  \n",
       "1469987           0  \n",
       "216547            0  \n",
       "32329             0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1216481    1.362\n",
       "1661539    1.402\n",
       "1469987    0.468\n",
       "216547     0.368\n",
       "32329      1.290\n",
       "Name: Global_active_power, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1383264, 8), (1383264,), (204928, 8), (204928,))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1383264, 8)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr  = RandomForestRegressor(n_estimators = 50,max_depth=50, oob_score=True, random_state=2017,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=50,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=-1,\n",
       "           oob_score=True, random_state=2017, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted1 = rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.049809357204385"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(predicted1,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.6528 , 5.6528 , 5.25592, ..., 5.25592, 5.25592, 5.25592])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = result.iloc[:row,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train = result.iloc[:row, :]\n",
    "train = shuffle(train)\n",
    "X_train = train.iloc[:, 1:]\n",
    "y_train = train.iloc[:, 0]\n",
    "X_test = result.iloc[row:, 1:]\n",
    "y_test = result.iloc[row:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1844352, 8), (1844352,), (204928, 8), (204928,))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], 1, X_train_scaled.shape[1]))\n",
    "X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], 1, X_test_scaled.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1844352, 1, 8), (1844352,), (204928, 1, 8), (204928,))"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_reshaped.shape, y_train.shape, X_test_reshaped.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_k = Sequential()\n",
    "# model_k.add(LSTM(1, input_shape=(1,8)))\n",
    "# model_k.add(Dense(1))\n",
    "# model_k.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_k.fit(X_train_reshaped, y_train, validation_split=0.05,epochs=1, batch_size=256, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_k.fit(X_train_reshaped, y_train, validation_split=0.05,epochs=10, batch_size=256, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], 1, X_test_scaled.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res = model_k.predict(X_test_reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_squared_error(res,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res = np.reshape(res, (res.size,))\n",
    "# print(res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(y_test[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(res[:100])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#     try:\n",
    "#         fig = plt.figure()\n",
    "#         ax = fig.add_subplot(111)\n",
    "#         ax.plot(y_test.values[:100])\n",
    "#         plt.plot(res[:100])\n",
    "#         plt.show()\n",
    "#     except Exception as e:\n",
    "#         print (str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_squared_error(res,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation Time :  0.050133705139160156\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "layers = [1, 50, 100, 1]\n",
    "\n",
    "model.add(LSTM(\n",
    "        layers[1],\n",
    "        input_shape=(None, 8),\n",
    "        return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(\n",
    "        layers[2],\n",
    "        return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(\n",
    "        layers[3]))\n",
    "model.add(Activation(\"linear\"))\n",
    "\n",
    "start = time.time()\n",
    "model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n",
    "print (\"Compilation Time : \", time.time() - start)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Apratim\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\models.py:944: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1752134 samples, validate on 92218 samples\n",
      "Epoch 1/10\n",
      "1752134/1752134 [==============================] - 71s 40us/step - loss: 0.0293 - val_loss: 0.0025\n",
      "Epoch 2/10\n",
      "1752134/1752134 [==============================] - 71s 40us/step - loss: 0.0097 - val_loss: 0.0029\n",
      "Epoch 3/10\n",
      "1752134/1752134 [==============================] - 70s 40us/step - loss: 0.0088 - val_loss: 0.0031\n",
      "Epoch 4/10\n",
      "1752134/1752134 [==============================] - 70s 40us/step - loss: 0.0082 - val_loss: 0.0028\n",
      "Epoch 5/10\n",
      "1752134/1752134 [==============================] - 70s 40us/step - loss: 0.0079 - val_loss: 0.0032\n",
      "Epoch 6/10\n",
      "1752134/1752134 [==============================] - 71s 41us/step - loss: 0.0077 - val_loss: 0.0020\n",
      "Epoch 7/10\n",
      "1752134/1752134 [==============================] - 72s 41us/step - loss: 0.0075 - val_loss: 0.0016\n",
      "Epoch 8/10\n",
      "1752134/1752134 [==============================] - 70s 40us/step - loss: 0.0073 - val_loss: 0.0016\n",
      "Epoch 9/10\n",
      "1752134/1752134 [==============================] - 57s 32us/step - loss: 0.0072 - val_loss: 0.0017\n",
      "Epoch 10/10\n",
      "1752134/1752134 [==============================] - 57s 33us/step - loss: 0.0071 - val_loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2dd8836ec88>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_reshaped, y_train,batch_size=512, nb_epoch=10, validation_split=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Apratim\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\models.py:944: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1752134 samples, validate on 92218 samples\n",
      "Epoch 1/10\n",
      "1752134/1752134 [==============================] - 71s 41us/step - loss: 0.0070 - val_loss: 0.0025\n",
      "Epoch 2/10\n",
      "1752134/1752134 [==============================] - 73s 41us/step - loss: 0.0070 - val_loss: 0.0058\n",
      "Epoch 3/10\n",
      "1752134/1752134 [==============================] - 72s 41us/step - loss: 0.0069 - val_loss: 0.0016\n",
      "Epoch 4/10\n",
      "1752134/1752134 [==============================] - 72s 41us/step - loss: 0.0068 - val_loss: 0.0016\n",
      "Epoch 5/10\n",
      "1752134/1752134 [==============================] - 76s 44us/step - loss: 0.0067 - val_loss: 0.0023\n",
      "Epoch 6/10\n",
      "1752134/1752134 [==============================] - 71s 40us/step - loss: 0.0066 - val_loss: 0.0021\n",
      "Epoch 7/10\n",
      "1752134/1752134 [==============================] - 72s 41us/step - loss: 0.0066 - val_loss: 0.0028\n",
      "Epoch 8/10\n",
      "1752134/1752134 [==============================] - 73s 42us/step - loss: 0.0065 - val_loss: 0.0044\n",
      "Epoch 9/10\n",
      "1752134/1752134 [==============================] - 71s 41us/step - loss: 0.0064 - val_loss: 0.0034\n",
      "Epoch 10/10\n",
      "1752134/1752134 [==============================] - 70s 40us/step - loss: 0.0064 - val_loss: 0.0021\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train_reshaped, y_train,batch_size=512, nb_epoch=10, validation_split=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2dd9b09fa58>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dd9e162e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "res2 = model.predict(X_test_reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, None, 50)          11800     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, None, 50)          0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 100)               60400     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 101       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 72,301\n",
      "Trainable params: 72,301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.46462228,  0.06874348,  0.0930337 ,  0.0953532 ,  0.11391216,\n",
       "         0.10927185,  0.04663354, -0.00382616]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_reshaped[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(204928,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "res2 = np.reshape(res2, (res2.size,))\n",
    "print(res2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dd9c42f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    try:\n",
    "        fig = plt.figure(figsize=(10,10))\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(y_test.values[:100],label='Actual Value')\n",
    "        plt.plot(res2[:100],label='Predicted Value')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    except Exception as e:\n",
    "        print (str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07959310370688759"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(res2,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((204928, 1, 8), (204928,))"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_reshaped.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204928,)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_test_window_preds(n_future_preds):\n",
    "\n",
    "    ''' n_future_preds - Represents the number of future predictions we want to make\n",
    "                         This coincides with the number of windows that we will move forward\n",
    "                         on the test data\n",
    "    '''\n",
    "    preds_moving = []                                    # Use this to store the prediction made on each test window\n",
    "    moving_test_window = [X_test_reshaped[0,:].tolist()]          # Creating the first test window\n",
    "    moving_test_window = np.array(moving_test_window)    # Making it an numpy array\n",
    "    \n",
    "    for i in range(n_future_preds):\n",
    "        preds_one_step = model.predict(moving_test_window) # Note that this is already a scaled prediction so no need to rescale this\n",
    "        preds_moving.append(preds_one_step[0,0]) # get the value from the numpy 2D array and append to predictions\n",
    "        preds_one_step = preds_one_step.reshape(1,1,1) # Reshaping the prediction to 3D array for concatenation with moving test window\n",
    "        moving_test_window = np.concatenate((moving_test_window[:,:,:], preds_one_step), axis=1) # This is the new moving test window, where the first element from the window has been removed and the prediction  has been appended to the end\n",
    "        \n",
    "    return preds_moving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-120-cf15b05e6515>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpreds_movin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmoving_test_window_preds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-119-e197dff387e7>\u001b[0m in \u001b[0;36mmoving_test_window_preds\u001b[1;34m(n_future_preds)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[0mpreds_moving\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds_one_step\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# get the value from the numpy 2D array and append to predictions\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mpreds_one_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreds_one_step\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Reshaping the prediction to 3D array for concatenation with moving test window\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0mmoving_test_window\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmoving_test_window\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds_one_step\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# This is the new moving test window, where the first element from the window has been removed and the prediction  has been appended to the end\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mpreds_moving\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
     ]
    }
   ],
   "source": [
    "preds_movin = moving_test_window_preds(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.46462228,  0.06874348,  0.0930337 ,  0.0953532 ,  0.11391216,\n",
       "         0.10927185,  0.04663354, -0.00382616]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_reshaped[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "moving_test_window=np.array([X_test_reshaped[0,:].tolist()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 8)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_test_window.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_moving = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_one_step = model.predict(moving_test_window) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_one_step.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_moving.append(preds_one_step[0,0]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-128-b34aa37d23f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpreds_moving\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "preds_moving.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_one_step = preds_one_step.reshape(1,1,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 1)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_one_step.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.1942595]]], dtype=float32)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_one_step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-0.46462228,  0.06874348,  0.0930337 ,  0.0953532 ,\n",
       "          0.11391216,  0.10927185,  0.04663354, -0.00382616]]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_test_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-0.46462228,  0.06874348,  0.0930337 ,  0.0953532 ,\n",
       "          0.11391216,  0.10927185,  0.04663354, -0.00382616]]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_test_window[:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-134-4486a9094543>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmoving_test_window\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds_one_step\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
     ]
    }
   ],
   "source": [
    "new = np.concatenate((moving_test_window[:,:,:], preds_one_step), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'concat'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-135-bb4c11eafda6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmoving_test_window\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds_one_step\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'concat'"
     ]
    }
   ],
   "source": [
    "moving_test_window.concat(preds_one_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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