Multivarate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.51\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(3.51,copy=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "# rest of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </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",
       "    </tr>\n",
       "    <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",
       "    </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",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>17.0</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",
       "    </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",
       "    </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  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075259"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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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": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "content.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "content[\"DATE_TIME\"] = pd.to_datetime(content.Date + \" \" + content[\"Time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <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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       "  <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",
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       "      <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",
       "      <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>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",
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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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.drop(['Date','Time','Global_reactive_power'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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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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       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
       "      <td>233.630</td>\n",
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       "      <td>0.000</td>\n",
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       "      <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",
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       "      <td>2006-12-16 17:26:00</td>\n",
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       "    <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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.drop(['Sub_metering_1','Sub_metering_2','Sub_metering_3'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075259"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.Voltage = pd.to_numeric(content.Voltage,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2049280"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.Global_intensity = pd.to_numeric(content.Global_intensity,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "content=content.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "content.Global_active_power = pd.to_numeric(content.Global_active_power,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "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",
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       "      <td>23.0</td>\n",
       "      <td>2006-12-16 17:26:00</td>\n",
       "      <td>5.360</td>\n",
       "      <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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.fillna(0.00,inplace=True)\n",
    "content.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "content[\"V_\" + str(1)] = content.Voltage.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "content[\"Global_intensity\" + str(1)] = content.Global_intensity.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Global_active_power</th>\n",
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       "      <th>3</th>\n",
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       "      <td>233.74</td>\n",
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       "      <td>2006-12-16 17:27:00</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>2006-12-16 17:31:00</td>\n",
       "      <td>3.702</td>\n",
       "      <td>3.520</td>\n",
       "      <td>3.666</td>\n",
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       "      <td>235.09</td>\n",
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       "    <tr>\n",
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       "      <td>3.668</td>\n",
       "      <td>233.99</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2006-12-16 17:32:00</td>\n",
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       "      <td>3.520</td>\n",
       "      <td>3.666</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",
       "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     V_1  Global_intensity1  \n",
       "0  0.000  0.000  0.000  0.000     NaN                NaN  \n",
       "1  0.000  0.000  0.000  0.000  234.84               18.4  \n",
       "2  4.216  0.000  0.000  0.000  233.63               23.0  \n",
       "3  5.360  4.216  0.000  0.000  233.29               23.0  \n",
       "4  5.374  5.360  4.216  0.000  233.74               23.0  \n",
       "5  5.388  5.374  5.360  4.216  235.68               15.8  \n",
       "6  3.666  5.388  5.374  5.360  235.02               15.0  \n",
       "7  3.520  3.666  5.388  5.374  235.09               15.8  \n",
       "8  3.702  3.520  3.666  5.388  235.22               15.8  \n",
       "9  3.700  3.702  3.520  3.666  233.99               15.8  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
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       "    <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",
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       "      <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",
       "      <td>235.22</td>\n",
       "      <td>15.8</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",
       "      <td>233.99</td>\n",
       "      <td>15.8</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     V_1  Global_intensity1  \n",
       "0  0.000  0.000  0.000  0.000    0.00                0.0  \n",
       "1  0.000  0.000  0.000  0.000  234.84               18.4  \n",
       "2  4.216  0.000  0.000  0.000  233.63               23.0  \n",
       "3  5.360  4.216  0.000  0.000  233.29               23.0  \n",
       "4  5.374  5.360  4.216  0.000  233.74               23.0  \n",
       "5  5.388  5.374  5.360  4.216  235.68               15.8  \n",
       "6  3.666  5.388  5.374  5.360  235.02               15.0  \n",
       "7  3.520  3.666  5.388  5.374  235.09               15.8  \n",
       "8  3.702  3.520  3.666  5.388  235.22               15.8  \n",
       "9  3.700  3.702  3.520  3.666  233.99               15.8  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.fillna(0.00,inplace=True)\n",
    "content.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "content = content.drop(['Voltage','Global_intensity'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2049280\n",
      "Global_active_power           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",
      "V_1                           float64\n",
      "Global_intensity1             float64\n",
      "dtype: object\n",
      "       Global_active_power           T_1           T_2           T_3  \\\n",
      "count         2.049280e+06  2.049280e+06  2.049280e+06  2.049280e+06   \n",
      "mean          1.091615e+00  1.091615e+00  1.091614e+00  1.091614e+00   \n",
      "std           1.057294e+00  1.057294e+00  1.057295e+00  1.057295e+00   \n",
      "min           7.600000e-02  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",
      "\n",
      "                T_4           T_5           V_1  Global_intensity1  \n",
      "count  2.049280e+06  2.049280e+06  2.049280e+06       2.049280e+06  \n",
      "mean   1.091613e+00  1.091613e+00  2.408397e+02       4.627757e+00  \n",
      "std    1.057295e+00  1.057296e+00  3.244352e+00       4.444397e+00  \n",
      "min    0.000000e+00  0.000000e+00  0.000000e+00       0.000000e+00  \n",
      "25%    3.080000e-01  3.080000e-01  2.389900e+02       1.400000e+00  \n",
      "50%    6.020000e-01  6.020000e-01  2.410100e+02       2.600000e+00  \n",
      "75%    1.528000e+00  1.528000e+00  2.428900e+02       6.400000e+00  \n",
      "max    1.112200e+01  1.112200e+01  2.541500e+02       4.840000e+01  \n"
     ]
    }
   ],
   "source": [
    "print(len(content))\n",
    "print(content.dtypes)\n",
    "print(content.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Global_active_power', 'DATE_TIME', 'T_1', 'T_2', 'T_3', 'T_4', 'T_5',\n",
       "       'V_1', 'Global_intensity1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_active_power</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>V_1</th>\n",
       "      <th>Global_intensity1</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.216</td>\n",
       "      <td>2006-12-16 17:24:00</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
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       "      <td>4.216</td>\n",
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       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</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>233.63</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</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>233.29</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</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>233.74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power           DATE_TIME    T_1    T_2    T_3    T_4  T_5  \\\n",
       "0                4.216 2006-12-16 17:24:00  0.000  0.000  0.000  0.000  0.0   \n",
       "1                5.360 2006-12-16 17:25:00  4.216  0.000  0.000  0.000  0.0   \n",
       "2                5.374 2006-12-16 17:26:00  5.360  4.216  0.000  0.000  0.0   \n",
       "3                5.388 2006-12-16 17:27:00  5.374  5.360  4.216  0.000  0.0   \n",
       "4                3.666 2006-12-16 17:28:00  5.388  5.374  5.360  4.216  0.0   \n",
       "\n",
       "      V_1  Global_intensity1  \n",
       "0    0.00                0.0  \n",
       "1  234.84               18.4  \n",
       "2  233.63               23.0  \n",
       "3  233.29               23.0  \n",
       "4  233.74               23.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Global_active_power</th>\n",
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       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
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       "      <th>Global_intensity1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2075254</th>\n",
       "      <td>0.946</td>\n",
       "      <td>2010-11-26 20:58:00</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.944</td>\n",
       "      <td>240.33</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075255</th>\n",
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       "      <td>240.43</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075256</th>\n",
       "      <td>0.938</td>\n",
       "      <td>2010-11-26 21:00:00</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.946</td>\n",
       "      <td>240.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075257</th>\n",
       "      <td>0.934</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",
       "      <td>239.82</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075258</th>\n",
       "      <td>0.932</td>\n",
       "      <td>2010-11-26 21:02:00</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "      <td>239.70</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Global_active_power           DATE_TIME    T_1    T_2    T_3    T_4  \\\n",
       "2075254                0.946 2010-11-26 20:58:00  0.946  0.942  0.946  0.946   \n",
       "2075255                0.944 2010-11-26 20:59:00  0.946  0.946  0.942  0.946   \n",
       "2075256                0.938 2010-11-26 21:00:00  0.944  0.946  0.946  0.942   \n",
       "2075257                0.934 2010-11-26 21:01:00  0.938  0.944  0.946  0.946   \n",
       "2075258                0.932 2010-11-26 21:02:00  0.934  0.938  0.944  0.946   \n",
       "\n",
       "           T_5     V_1  Global_intensity1  \n",
       "2075254  0.944  240.33                4.0  \n",
       "2075255  0.946  240.43                4.0  \n",
       "2075256  0.946  240.00                4.0  \n",
       "2075257  0.942  239.82                3.8  \n",
       "2075258  0.946  239.70                3.8  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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>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>V_1</th>\n",
       "      <th>Global_intensity1</th>\n",
       "      <th>IS_HOLIDAY</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.360</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</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>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</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>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</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>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power           DATE_TIME    T_1    T_2    T_3    T_4  T_5  \\\n",
       "0                4.216 2006-12-16 17:24:00  0.000  0.000  0.000  0.000  0.0   \n",
       "1                5.360 2006-12-16 17:25:00  4.216  0.000  0.000  0.000  0.0   \n",
       "2                5.374 2006-12-16 17:26:00  5.360  4.216  0.000  0.000  0.0   \n",
       "3                5.388 2006-12-16 17:27:00  5.374  5.360  4.216  0.000  0.0   \n",
       "4                3.666 2006-12-16 17:28:00  5.388  5.374  5.360  4.216  0.0   \n",
       "\n",
       "      V_1  Global_intensity1  IS_HOLIDAY  \n",
       "0    0.00                0.0       False  \n",
       "1  234.84               18.4       False  \n",
       "2  233.63               23.0       False  \n",
       "3  233.29               23.0       False  \n",
       "4  233.74               23.0       False  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>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>V_1</th>\n",
       "      <th>Global_intensity1</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>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>0.00</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>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>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374</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>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.388</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>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.666</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>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_active_power           DATE_TIME    T_1    T_2    T_3    T_4  T_5  \\\n",
       "0                4.216 2006-12-16 17:24:00  0.000  0.000  0.000  0.000  0.0   \n",
       "1                5.360 2006-12-16 17:25:00  4.216  0.000  0.000  0.000  0.0   \n",
       "2                5.374 2006-12-16 17:26:00  5.360  4.216  0.000  0.000  0.0   \n",
       "3                5.388 2006-12-16 17:27:00  5.374  5.360  4.216  0.000  0.0   \n",
       "4                3.666 2006-12-16 17:28:00  5.388  5.374  5.360  4.216  0.0   \n",
       "\n",
       "      V_1  Global_intensity1  IS_HOLIDAY  \n",
       "0    0.00                0.0           0  \n",
       "1  234.84               18.4           0  \n",
       "2  233.63               23.0           0  \n",
       "3  233.29               23.0           0  \n",
       "4  233.74               23.0           0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=result.drop(['DATE_TIME'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    float64\n",
       "T_1                    float64\n",
       "T_2                    float64\n",
       "T_3                    float64\n",
       "T_4                    float64\n",
       "T_5                    float64\n",
       "V_1                    float64\n",
       "Global_intensity1      float64\n",
       "IS_HOLIDAY               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2049280"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = int(round(0.9 * result.shape[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1844352"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = result.iloc[:row,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1844352"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "train = shuffle(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Global_active_power</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>V_1</th>\n",
       "      <th>Global_intensity1</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>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>246.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782331</th>\n",
       "      <td>1.348</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>235.49</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407334</th>\n",
       "      <td>0.708</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>238.49</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709060</th>\n",
       "      <td>0.354</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>243.18</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957163</th>\n",
       "      <td>1.282</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>236.94</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Global_active_power    T_1    T_2    T_3    T_4    T_5     V_1  \\\n",
       "4794                   0.210  0.228  0.268  0.298  0.280  0.298  246.94   \n",
       "1782331                1.348  1.340  1.306  1.256  1.256  1.264  235.49   \n",
       "407334                 0.708  0.728  0.708  0.728  0.706  0.724  238.49   \n",
       "709060                 0.354  0.350  0.372  0.698  1.004  0.984  243.18   \n",
       "957163                 1.282  1.272  1.268  1.278  1.276  1.284  236.94   \n",
       "\n",
       "         Global_intensity1  IS_HOLIDAY  \n",
       "4794                   1.0           0  \n",
       "1782331                5.6           0  \n",
       "407334                 3.0           0  \n",
       "709060                 1.4           0  \n",
       "957163                 5.2           0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power    float64\n",
       "T_1                    float64\n",
       "T_2                    float64\n",
       "T_3                    float64\n",
       "T_4                    float64\n",
       "T_5                    float64\n",
       "V_1                    float64\n",
       "Global_intensity1      float64\n",
       "IS_HOLIDAY               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.sample(frac=0.75, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>V_1</th>\n",
       "      <th>Global_intensity1</th>\n",
       "      <th>IS_HOLIDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1216481</th>\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>242.76</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661539</th>\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>241.70</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469987</th>\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>241.96</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216547</th>\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>235.49</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32329</th>\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>241.47</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           T_1    T_2    T_3    T_4    T_5     V_1  Global_intensity1  \\\n",
       "1216481  1.356  1.356  1.370  1.380  1.472  242.76                5.6   \n",
       "1661539  1.404  1.426  1.420  1.424  1.422  241.70                5.8   \n",
       "1469987  0.474  0.472  0.474  0.472  0.474  241.96                2.0   \n",
       "216547   0.368  0.368  0.356  0.280  0.210  235.49                1.6   \n",
       "32329    1.310  1.294  1.308  1.284  1.304  241.47                5.4   \n",
       "\n",
       "         IS_HOLIDAY  \n",
       "1216481           0  \n",
       "1661539           0  \n",
       "1469987           0  \n",
       "216547            0  \n",
       "32329             0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1383264, 8), (1383264,), (204928, 8), (204928,))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1383264, 8)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted1 = rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3396967412490968"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(predicted1,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.57412, 3.52804, 3.30948, ..., 2.92144, 2.778  , 2.62752])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = result.iloc[:row,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1844352, 8), (1844352,), (204928, 8), (204928,))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": 86,
   "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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1844352, 1, 8), (1844352,), (204928, 1, 8), (204928,))"
      ]
     },
     "execution_count": 87,
     "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": 88,
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1752134 samples, validate on 92218 samples\n",
      "Epoch 1/1\n",
      " - 14s - loss: 0.0811 - val_loss: 0.0799\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x29302c72fd0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1752134 samples, validate on 92218 samples\n",
      "Epoch 1/10\n",
      " - 15s - loss: 0.0748 - val_loss: 0.0774\n",
      "Epoch 2/10\n",
      " - 16s - loss: 0.0734 - val_loss: 0.0775\n",
      "Epoch 3/10\n",
      " - 15s - loss: 0.0728 - val_loss: 0.0769\n",
      "Epoch 4/10\n",
      " - 20s - loss: 0.0725 - val_loss: 0.0758\n",
      "Epoch 5/10\n",
      " - 16s - loss: 0.0723 - val_loss: 0.0786\n",
      "Epoch 6/10\n",
      " - 15s - loss: 0.0722 - val_loss: 0.0758\n",
      "Epoch 7/10\n",
      " - 16s - loss: 0.0721 - val_loss: 0.0766\n",
      "Epoch 8/10\n",
      " - 16s - loss: 0.0720 - val_loss: 0.0754\n",
      "Epoch 9/10\n",
      " - 17s - loss: 0.0719 - val_loss: 0.0752\n",
      "Epoch 10/10\n",
      " - 16s - loss: 0.0719 - val_loss: 0.0754\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x29302a42fd0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 92,
   "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model_k.predict(X_test_reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1294262867554982"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(res,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204928, 1)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204928,)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(204928,)\n"
     ]
    }
   ],
   "source": [
    "res = np.reshape(res, (res.size,))\n",
    "print(res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29315ccf8d0>]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29313ffcb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29315496dd8>]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x293153af978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:100])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.2149413, 1.2149339, 1.2050824, ..., 1.1404221, 1.1169012,\n",
       "       1.1144266], dtype=float32)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29315494f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1294262867554982"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(res,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation Time :  0.024063825607299805\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": 121,
   "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 1/1\n",
      "1752134/1752134 [==============================] - 57s 32us/step - loss: 0.0968 - val_loss: 0.0792\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x293150d9cf8>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_reshaped, y_train,batch_size=512, nb_epoch=1, validation_split=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "res2 = model.predict(X_test_reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0930337 ,  0.0953532 ,  0.11391216,  0.10927185,  0.04663354,\n",
       "        -0.3804366 ,  0.06874294, -0.00382616]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_reshaped[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x293483fac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    try:\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(y_test.values[:100])\n",
    "        plt.plot(res2[:100])\n",
    "        plt.show()\n",
    "    except Exception as e:\n",
    "        print (str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08847955069261022"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(res2,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((204928, 1, 8), (204928,))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_reshaped.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204928,)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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": 141,
   "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-141-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-140-ff7dec909fb4>\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;36m1\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": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0930337 ,  0.0953532 ,  0.11391216,  0.10927185,  0.04663354,\n",
       "        -0.3804366 ,  0.06874294, -0.00382616]])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_reshaped[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "moving_test_window=np.array([X_test_reshaped[0,:].tolist()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 8)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_test_window.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_moving = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_one_step = model.predict(moving_test_window) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_one_step.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_moving.append(preds_one_step[0,0]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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-186-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": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_one_step = preds_one_step.reshape(1,1,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 1)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_one_step.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.1879075]]], dtype=float32)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_one_step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.0930337 ,  0.0953532 ,  0.11391216,  0.10927185,\n",
       "          0.04663354, -0.3804366 ,  0.06874294, -0.00382616]]])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_test_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.0930337 ,  0.0953532 ,  0.11391216,  0.10927185,\n",
       "          0.04663354, -0.3804366 ,  0.06874294, -0.00382616]]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_test_window[:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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-198-4486a9094543>\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": 201,
   "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-201-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": []
  }
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