CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Importing the dataset\n",
    "\n",
    "#from pandas.plotting import scatter_matrix\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Common seed value to be used whenever required\n",
    "seed = 79\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[\"T1\", \"T2\", \"T3\", \"T4\", \"T5\", \"T6\", \"T7\", \"T8\", \"T9\",\"RH_1\", \"RH_2\", \"RH_3\", \"RH_4\", \"RH_5\", \"RH_6\", \"RH_7\", \"RH_8\", \"RH_9\",\"T_out\", \"Tdewpoint\", \"RH_out\", \"Press_mm_hg\", \"Windspeed\", \"Visibility\",\"Appliances\"]\n",
    "#load the datasets\n",
    "energy = pd.read_csv(r\"C:\\Users\\chandan sharma\\Desktop\\research\\-------------MOST\\final project\\Data-driven-prediction-models-of-energy-use-of-appliances-in-a-low-energy-house-master\\dataset\\training.csv\",usecols=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(r\"C:\\Users\\chandan sharma\\Desktop\\research\\-------------MOST\\final project\\Data-driven-prediction-models-of-energy-use-of-appliances-in-a-low-energy-house-master\\dataset\\testing_validation.csv\",usecols=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from time import time\n",
    "\n",
    "# Prepare the data\n",
    "X_train = energy.drop(\"Appliances\", axis=1)\n",
    "y_train = energy[\"Appliances\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the features and the target variable\n",
    "X_test = test.drop(\"Appliances\", axis=1)\n",
    "y_test = test[\"Appliances\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T5</th>\n",
       "      <th>...</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "      <td>14803.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>98.011214</td>\n",
       "      <td>21.684167</td>\n",
       "      <td>40.271439</td>\n",
       "      <td>20.342506</td>\n",
       "      <td>40.418056</td>\n",
       "      <td>22.262628</td>\n",
       "      <td>39.249149</td>\n",
       "      <td>20.855433</td>\n",
       "      <td>39.030359</td>\n",
       "      <td>19.585043</td>\n",
       "      <td>...</td>\n",
       "      <td>22.029160</td>\n",
       "      <td>42.924554</td>\n",
       "      <td>19.480759</td>\n",
       "      <td>41.542065</td>\n",
       "      <td>7.413018</td>\n",
       "      <td>755.502983</td>\n",
       "      <td>79.734122</td>\n",
       "      <td>4.034470</td>\n",
       "      <td>38.330141</td>\n",
       "      <td>3.756859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102.828019</td>\n",
       "      <td>1.607780</td>\n",
       "      <td>3.981741</td>\n",
       "      <td>2.191842</td>\n",
       "      <td>4.066223</td>\n",
       "      <td>2.013785</td>\n",
       "      <td>3.252755</td>\n",
       "      <td>2.044786</td>\n",
       "      <td>4.339783</td>\n",
       "      <td>1.847725</td>\n",
       "      <td>...</td>\n",
       "      <td>1.958897</td>\n",
       "      <td>5.242376</td>\n",
       "      <td>2.017733</td>\n",
       "      <td>4.150839</td>\n",
       "      <td>5.323843</td>\n",
       "      <td>7.427684</td>\n",
       "      <td>14.955609</td>\n",
       "      <td>2.436870</td>\n",
       "      <td>11.812780</td>\n",
       "      <td>4.200297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>16.790000</td>\n",
       "      <td>27.023333</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>20.893333</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>28.766667</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>27.660000</td>\n",
       "      <td>15.330000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.306667</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>729.300000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-6.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>20.730000</td>\n",
       "      <td>37.361667</td>\n",
       "      <td>18.823333</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>20.790000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>35.530000</td>\n",
       "      <td>18.265000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.790000</td>\n",
       "      <td>39.060000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>750.866667</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>39.656667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>38.530000</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.128571</td>\n",
       "      <td>42.363333</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>40.863333</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>756.100000</td>\n",
       "      <td>83.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>43.090000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>43.290000</td>\n",
       "      <td>23.290000</td>\n",
       "      <td>41.761667</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>42.130000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.390000</td>\n",
       "      <td>46.561667</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>44.363333</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>760.933333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1080.000000</td>\n",
       "      <td>26.260000</td>\n",
       "      <td>63.360000</td>\n",
       "      <td>29.856667</td>\n",
       "      <td>56.026667</td>\n",
       "      <td>29.236000</td>\n",
       "      <td>50.163333</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>51.063333</td>\n",
       "      <td>25.745000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.230000</td>\n",
       "      <td>58.780000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>53.326667</td>\n",
       "      <td>25.966667</td>\n",
       "      <td>772.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "         Appliances            T1          RH_1            T2          RH_2  \\\n",
       "count  14803.000000  14803.000000  14803.000000  14803.000000  14803.000000   \n",
       "mean      98.011214     21.684167     40.271439     20.342506     40.418056   \n",
       "std      102.828019      1.607780      3.981741      2.191842      4.066223   \n",
       "min       10.000000     16.790000     27.023333     16.100000     20.893333   \n",
       "25%       50.000000     20.730000     37.361667     18.823333     37.900000   \n",
       "50%       60.000000     21.600000     39.656667     20.000000     40.500000   \n",
       "75%      100.000000     22.600000     43.090000     21.500000     43.290000   \n",
       "max     1080.000000     26.260000     63.360000     29.856667     56.026667   \n",
       "\n",
       "                 T3          RH_3            T4          RH_4            T5  \\\n",
       "count  14803.000000  14803.000000  14803.000000  14803.000000  14803.000000   \n",
       "mean      22.262628     39.249149     20.855433     39.030359     19.585043   \n",
       "std        2.013785      3.252755      2.044786      4.339783      1.847725   \n",
       "min       17.200000     28.766667     15.100000     27.660000     15.330000   \n",
       "25%       20.790000     36.900000     19.500000     35.530000     18.265000   \n",
       "50%       22.100000     38.530000     20.666667     38.400000     19.390000   \n",
       "75%       23.290000     41.761667     22.100000     42.130000     20.600000   \n",
       "max       29.236000     50.163333     26.200000     51.063333     25.745000   \n",
       "\n",
       "           ...                 T8          RH_8            T9          RH_9  \\\n",
       "count      ...       14803.000000  14803.000000  14803.000000  14803.000000   \n",
       "mean       ...          22.029160     42.924554     19.480759     41.542065   \n",
       "std        ...           1.958897      5.242376      2.017733      4.150839   \n",
       "min        ...          16.306667     29.600000     14.890000     29.166667   \n",
       "25%        ...          20.790000     39.060000     18.000000     38.500000   \n",
       "50%        ...          22.128571     42.363333     19.390000     40.863333   \n",
       "75%        ...          23.390000     46.561667     20.600000     44.363333   \n",
       "max        ...          27.230000     58.780000     24.500000     53.326667   \n",
       "\n",
       "              T_out   Press_mm_hg        RH_out     Windspeed    Visibility  \\\n",
       "count  14803.000000  14803.000000  14803.000000  14803.000000  14803.000000   \n",
       "mean       7.413018    755.502983     79.734122      4.034470     38.330141   \n",
       "std        5.323843      7.427684     14.955609      2.436870     11.812780   \n",
       "min       -5.000000    729.300000     24.000000      0.000000      1.000000   \n",
       "25%        3.666667    750.866667     70.000000      2.000000     29.000000   \n",
       "50%        6.900000    756.100000     83.666667      3.666667     40.000000   \n",
       "75%       10.400000    760.933333     91.666667      5.500000     40.000000   \n",
       "max       25.966667    772.300000    100.000000     13.500000     66.000000   \n",
       "\n",
       "          Tdewpoint  \n",
       "count  14803.000000  \n",
       "mean       3.756859  \n",
       "std        4.200297  \n",
       "min       -6.600000  \n",
       "25%        0.900000  \n",
       "50%        3.450000  \n",
       "75%        6.533333  \n",
       "max       15.500000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "      <th>T1</th>\n",
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       "      <th>RH_2</th>\n",
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       "      <th>RH_8</th>\n",
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       "      <th>Visibility</th>\n",
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       "      <th>0</th>\n",
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       "      <td>19.200000</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>45.723333</td>\n",
       "      <td>17.166667</td>\n",
       "      <td>55.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>6.250</td>\n",
       "      <td>733.800000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.890000</td>\n",
       "      <td>45.766667</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>45.790000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>54.966667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>5.900</td>\n",
       "      <td>734.100000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.7000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.926667</td>\n",
       "      <td>45.863333</td>\n",
       "      <td>19.356667</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>46.430000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>5.970</td>\n",
       "      <td>734.366667</td>\n",
       "      <td>91.333333</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.066667</td>\n",
       "      <td>46.396667</td>\n",
       "      <td>19.426667</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.826667</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>46.430000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.066667</td>\n",
       "      <td>48.633333</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>5.980</td>\n",
       "      <td>734.433333</td>\n",
       "      <td>91.166667</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.566667</td>\n",
       "      <td>53.893333</td>\n",
       "      <td>20.033333</td>\n",
       "      <td>46.756667</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>48.466667</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>48.490000</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>56.042500</td>\n",
       "      <td>...</td>\n",
       "      <td>18.150000</td>\n",
       "      <td>49.200000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>45.326667</td>\n",
       "      <td>6.000</td>\n",
       "      <td>734.850000</td>\n",
       "      <td>89.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.856667</td>\n",
       "      <td>53.660000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>47.056667</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>48.447500</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>47.963333</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>56.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>18.356667</td>\n",
       "      <td>50.026667</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>735.083333</td>\n",
       "      <td>88.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.963333</td>\n",
       "      <td>47.633333</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>45.530000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>47.026667</td>\n",
       "      <td>19.260000</td>\n",
       "      <td>49.696667</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>79.730000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.856667</td>\n",
       "      <td>50.290000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>45.126667</td>\n",
       "      <td>6.000</td>\n",
       "      <td>735.300000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21.033333</td>\n",
       "      <td>47.063333</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>45.223333</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>46.826667</td>\n",
       "      <td>19.323333</td>\n",
       "      <td>49.166667</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>79.256667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>50.163333</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>45.060000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>735.333333</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.356667</td>\n",
       "      <td>45.826667</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>45.163333</td>\n",
       "      <td>20.390000</td>\n",
       "      <td>46.090000</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>62.430000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>49.433333</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>5.800</td>\n",
       "      <td>735.600000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21.600000</td>\n",
       "      <td>44.200000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>43.700000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>20.996667</td>\n",
       "      <td>47.030000</td>\n",
       "      <td>19.222500</td>\n",
       "      <td>51.520000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>49.230000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>5.850</td>\n",
       "      <td>736.216667</td>\n",
       "      <td>87.166667</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.166667</td>\n",
       "      <td>45.230000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>44.126667</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>20.460000</td>\n",
       "      <td>47.360000</td>\n",
       "      <td>19.066667</td>\n",
       "      <td>50.260000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.356667</td>\n",
       "      <td>52.090000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>46.030000</td>\n",
       "      <td>5.130</td>\n",
       "      <td>737.400000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>20.390000</td>\n",
       "      <td>44.200000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>21.263333</td>\n",
       "      <td>47.466667</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>50.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>46.200000</td>\n",
       "      <td>5.120</td>\n",
       "      <td>737.633333</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.926667</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>44.290000</td>\n",
       "      <td>20.166667</td>\n",
       "      <td>45.530000</td>\n",
       "      <td>21.890000</td>\n",
       "      <td>46.966667</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>50.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>46.290000</td>\n",
       "      <td>5.150</td>\n",
       "      <td>737.900000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.700000</td>\n",
       "      <td>46.260000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>20.133333</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>21.230000</td>\n",
       "      <td>45.900000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>50.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>54.966667</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>47.230000</td>\n",
       "      <td>5.070</td>\n",
       "      <td>738.500000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.600000</td>\n",
       "      <td>46.126667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>20.745000</td>\n",
       "      <td>45.900000</td>\n",
       "      <td>18.730000</td>\n",
       "      <td>50.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.133333</td>\n",
       "      <td>54.790000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>47.663333</td>\n",
       "      <td>4.870</td>\n",
       "      <td>738.800000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20.500000</td>\n",
       "      <td>46.290000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>20.390000</td>\n",
       "      <td>46.030000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>50.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>55.090000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>48.030000</td>\n",
       "      <td>4.830</td>\n",
       "      <td>739.133333</td>\n",
       "      <td>92.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20.390000</td>\n",
       "      <td>46.363333</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>20.230000</td>\n",
       "      <td>46.260000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>54.730000</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>48.363333</td>\n",
       "      <td>4.880</td>\n",
       "      <td>739.483333</td>\n",
       "      <td>92.833333</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20.290000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>44.700000</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>46.290000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>54.560000</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>48.463333</td>\n",
       "      <td>4.900</td>\n",
       "      <td>739.600000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.100000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>19.426667</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>46.663333</td>\n",
       "      <td>18.472500</td>\n",
       "      <td>49.975000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>54.230000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>49.290000</td>\n",
       "      <td>4.980</td>\n",
       "      <td>739.966667</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.033333</td>\n",
       "      <td>46.360000</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>46.663333</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>49.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>54.045000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>49.363333</td>\n",
       "      <td>4.970</td>\n",
       "      <td>740.033333</td>\n",
       "      <td>92.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19.926667</td>\n",
       "      <td>46.230000</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>20.323333</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>49.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.033333</td>\n",
       "      <td>53.826667</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>49.700000</td>\n",
       "      <td>4.900</td>\n",
       "      <td>740.300000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.890000</td>\n",
       "      <td>46.230000</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>44.925000</td>\n",
       "      <td>20.323333</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>18.356667</td>\n",
       "      <td>49.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>53.790000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>49.826667</td>\n",
       "      <td>4.930</td>\n",
       "      <td>740.433333</td>\n",
       "      <td>94.666667</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>38.166667</td>\n",
       "      <td>4.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.790000</td>\n",
       "      <td>46.790000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>45.326667</td>\n",
       "      <td>20.166667</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>46.596667</td>\n",
       "      <td>18.230000</td>\n",
       "      <td>50.833333</td>\n",
       "      <td>...</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>53.800000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>49.963333</td>\n",
       "      <td>5.100</td>\n",
       "      <td>741.333333</td>\n",
       "      <td>92.333333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>32.666667</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19.790000</td>\n",
       "      <td>48.366667</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>19.166667</td>\n",
       "      <td>45.966667</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>51.826667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.856667</td>\n",
       "      <td>52.363333</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>48.995000</td>\n",
       "      <td>5.100</td>\n",
       "      <td>741.683333</td>\n",
       "      <td>91.333333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>38.166667</td>\n",
       "      <td>3.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19.700000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>44.230000</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>19.166667</td>\n",
       "      <td>45.026667</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>49.656667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.533333</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.730000</td>\n",
       "      <td>5.480</td>\n",
       "      <td>742.883333</td>\n",
       "      <td>90.166667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.833333</td>\n",
       "      <td>3.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19.890000</td>\n",
       "      <td>47.160000</td>\n",
       "      <td>19.033333</td>\n",
       "      <td>44.566667</td>\n",
       "      <td>19.926667</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>20.066667</td>\n",
       "      <td>44.860000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>49.566667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>49.090000</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.230000</td>\n",
       "      <td>6.220</td>\n",
       "      <td>743.216667</td>\n",
       "      <td>86.833333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>38.166667</td>\n",
       "      <td>4.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20.100000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>43.900000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>43.826667</td>\n",
       "      <td>17.963333</td>\n",
       "      <td>49.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>47.352222</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>44.863333</td>\n",
       "      <td>7.250</td>\n",
       "      <td>743.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20.166667</td>\n",
       "      <td>46.993333</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>43.760000</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>44.163333</td>\n",
       "      <td>17.890000</td>\n",
       "      <td>50.560000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>47.126667</td>\n",
       "      <td>17.166667</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>7.500</td>\n",
       "      <td>743.500000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20.066667</td>\n",
       "      <td>45.363333</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>43.745000</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>44.863333</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>43.863333</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>50.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>47.330000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>7.250</td>\n",
       "      <td>743.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>44.260000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>43.290000</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>43.400000</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>46.863333</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>44.200000</td>\n",
       "      <td>6.970</td>\n",
       "      <td>743.600000</td>\n",
       "      <td>79.333333</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>19.066667</td>\n",
       "      <td>36.966667</td>\n",
       "      <td>16.856667</td>\n",
       "      <td>39.466667</td>\n",
       "      <td>19.533333</td>\n",
       "      <td>38.433333</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>17.290000</td>\n",
       "      <td>55.760000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>43.700000</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>41.290000</td>\n",
       "      <td>0.167</td>\n",
       "      <td>747.266667</td>\n",
       "      <td>99.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>16.790000</td>\n",
       "      <td>39.400000</td>\n",
       "      <td>19.533333</td>\n",
       "      <td>38.560000</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>17.230000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.463333</td>\n",
       "      <td>43.700000</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>41.363333</td>\n",
       "      <td>0.200</td>\n",
       "      <td>747.300000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>37.230000</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>39.760000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>38.326667</td>\n",
       "      <td>18.290000</td>\n",
       "      <td>36.700000</td>\n",
       "      <td>17.340000</td>\n",
       "      <td>53.690000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.390000</td>\n",
       "      <td>43.433333</td>\n",
       "      <td>17.633333</td>\n",
       "      <td>41.326667</td>\n",
       "      <td>0.267</td>\n",
       "      <td>747.566667</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.1670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>18.890000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>16.890000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>37.463333</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>36.260000</td>\n",
       "      <td>17.726667</td>\n",
       "      <td>54.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>41.566667</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>39.860000</td>\n",
       "      <td>0.633</td>\n",
       "      <td>747.833333</td>\n",
       "      <td>97.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.3330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>18.926667</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>39.826667</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>37.400000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>35.950000</td>\n",
       "      <td>18.033333</td>\n",
       "      <td>52.426667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.033333</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>17.533333</td>\n",
       "      <td>38.656667</td>\n",
       "      <td>0.967</td>\n",
       "      <td>748.000000</td>\n",
       "      <td>96.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.333333</td>\n",
       "      <td>0.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>17.356667</td>\n",
       "      <td>39.433333</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>35.900000</td>\n",
       "      <td>17.730000</td>\n",
       "      <td>50.656667</td>\n",
       "      <td>...</td>\n",
       "      <td>19.963333</td>\n",
       "      <td>39.626667</td>\n",
       "      <td>17.533333</td>\n",
       "      <td>37.933333</td>\n",
       "      <td>1.300</td>\n",
       "      <td>748.200000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>19.356667</td>\n",
       "      <td>39.526667</td>\n",
       "      <td>17.730000</td>\n",
       "      <td>39.163333</td>\n",
       "      <td>20.360000</td>\n",
       "      <td>40.156667</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>35.700000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>50.545000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>39.222500</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>37.466667</td>\n",
       "      <td>1.900</td>\n",
       "      <td>748.466667</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>39.666667</td>\n",
       "      <td>0.9670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>20.050000</td>\n",
       "      <td>46.400000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>40.060000</td>\n",
       "      <td>21.066667</td>\n",
       "      <td>41.426667</td>\n",
       "      <td>18.166667</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>50.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>38.433333</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>37.090000</td>\n",
       "      <td>3.120</td>\n",
       "      <td>748.766667</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.6830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>20.100000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>18.566667</td>\n",
       "      <td>40.630000</td>\n",
       "      <td>21.263333</td>\n",
       "      <td>42.633333</td>\n",
       "      <td>18.166667</td>\n",
       "      <td>35.626667</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>50.290000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>37.090000</td>\n",
       "      <td>3.300</td>\n",
       "      <td>748.800000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>20.600000</td>\n",
       "      <td>41.030000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>39.230000</td>\n",
       "      <td>21.960000</td>\n",
       "      <td>39.426667</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>35.400000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>50.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>37.790000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>36.700000</td>\n",
       "      <td>3.970</td>\n",
       "      <td>748.933333</td>\n",
       "      <td>74.333333</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>-0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>21.245000</td>\n",
       "      <td>40.795000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>37.950000</td>\n",
       "      <td>22.890000</td>\n",
       "      <td>38.450000</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>35.126667</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>37.090000</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>37.090000</td>\n",
       "      <td>5.500</td>\n",
       "      <td>749.200000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>21.390000</td>\n",
       "      <td>42.626667</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>38.133333</td>\n",
       "      <td>22.633333</td>\n",
       "      <td>37.933333</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>35.026667</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>49.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>5.070</td>\n",
       "      <td>749.266667</td>\n",
       "      <td>74.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>21.463333</td>\n",
       "      <td>39.466667</td>\n",
       "      <td>19.963333</td>\n",
       "      <td>38.133333</td>\n",
       "      <td>23.133333</td>\n",
       "      <td>38.490000</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>34.290000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>49.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>36.163333</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>36.966667</td>\n",
       "      <td>3.770</td>\n",
       "      <td>749.500000</td>\n",
       "      <td>78.166667</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>0.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>21.260000</td>\n",
       "      <td>38.090000</td>\n",
       "      <td>19.633333</td>\n",
       "      <td>37.530000</td>\n",
       "      <td>22.290000</td>\n",
       "      <td>36.163333</td>\n",
       "      <td>18.290000</td>\n",
       "      <td>34.790000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>48.230000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>36.723333</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>37.566667</td>\n",
       "      <td>3.180</td>\n",
       "      <td>750.333333</td>\n",
       "      <td>88.166667</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>1.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>37.933333</td>\n",
       "      <td>21.760000</td>\n",
       "      <td>35.900000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>17.463333</td>\n",
       "      <td>47.430000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>38.230000</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>1.400</td>\n",
       "      <td>751.300000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>21.100000</td>\n",
       "      <td>42.423333</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>38.956667</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>35.963333</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>34.966667</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>47.126667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.823333</td>\n",
       "      <td>38.163333</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>36.200000</td>\n",
       "      <td>1.930</td>\n",
       "      <td>751.633333</td>\n",
       "      <td>92.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.666667</td>\n",
       "      <td>0.8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>21.133333</td>\n",
       "      <td>41.296667</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>36.633333</td>\n",
       "      <td>18.066667</td>\n",
       "      <td>34.930000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>47.526667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.823333</td>\n",
       "      <td>38.126667</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>2.200</td>\n",
       "      <td>751.800000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>21.290000</td>\n",
       "      <td>39.096667</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>39.930000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>36.760000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>34.790000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>47.790000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>38.090000</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>35.863333</td>\n",
       "      <td>1.780</td>\n",
       "      <td>752.216667</td>\n",
       "      <td>91.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>21.290000</td>\n",
       "      <td>38.225000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>39.163333</td>\n",
       "      <td>21.463333</td>\n",
       "      <td>36.526667</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>35.096667</td>\n",
       "      <td>17.417500</td>\n",
       "      <td>47.617500</td>\n",
       "      <td>...</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>38.290000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>752.550000</td>\n",
       "      <td>92.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>21.290000</td>\n",
       "      <td>37.933333</td>\n",
       "      <td>19.133333</td>\n",
       "      <td>39.090000</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>35.045000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>47.475000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>38.363333</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>35.700000</td>\n",
       "      <td>1.100</td>\n",
       "      <td>752.633333</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>0.1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>21.100000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>38.326667</td>\n",
       "      <td>21.166667</td>\n",
       "      <td>36.790000</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>34.760000</td>\n",
       "      <td>18.130000</td>\n",
       "      <td>68.930000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.856667</td>\n",
       "      <td>38.626667</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>35.466667</td>\n",
       "      <td>0.633</td>\n",
       "      <td>753.133333</td>\n",
       "      <td>95.666667</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>20.890000</td>\n",
       "      <td>37.766667</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>39.966667</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>36.966667</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>35.826667</td>\n",
       "      <td>18.230000</td>\n",
       "      <td>50.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>39.690000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>36.260000</td>\n",
       "      <td>0.433</td>\n",
       "      <td>753.533333</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>56.833333</td>\n",
       "      <td>-0.0833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>20.790000</td>\n",
       "      <td>38.030000</td>\n",
       "      <td>18.290000</td>\n",
       "      <td>40.400000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>36.933333</td>\n",
       "      <td>20.890000</td>\n",
       "      <td>36.290000</td>\n",
       "      <td>18.290000</td>\n",
       "      <td>48.696667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>40.800000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>36.466667</td>\n",
       "      <td>0.350</td>\n",
       "      <td>753.666667</td>\n",
       "      <td>96.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>-0.1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>20.390000</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>17.856667</td>\n",
       "      <td>40.966667</td>\n",
       "      <td>20.963333</td>\n",
       "      <td>37.090000</td>\n",
       "      <td>21.066667</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.356667</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>0.800</td>\n",
       "      <td>754.300000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>20.290000</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>17.790000</td>\n",
       "      <td>41.060000</td>\n",
       "      <td>20.890000</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>20.760000</td>\n",
       "      <td>36.290000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>44.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>45.530000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>38.060000</td>\n",
       "      <td>1.070</td>\n",
       "      <td>754.366667</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>43.666667</td>\n",
       "      <td>0.8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>20.200000</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>17.633333</td>\n",
       "      <td>41.126667</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>20.230000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>43.730000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>45.560000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>38.530000</td>\n",
       "      <td>0.650</td>\n",
       "      <td>754.500000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>0.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>20.100000</td>\n",
       "      <td>38.326667</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>41.060000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>20.033333</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>43.590000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>45.433333</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>38.790000</td>\n",
       "      <td>0.283</td>\n",
       "      <td>754.566667</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>0.0833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>38.200000</td>\n",
       "      <td>17.566667</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.230000</td>\n",
       "      <td>19.856667</td>\n",
       "      <td>36.363333</td>\n",
       "      <td>18.133333</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>38.933333</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>754.666667</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.666667</td>\n",
       "      <td>-0.2330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>19.890000</td>\n",
       "      <td>38.090000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>40.900000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.290000</td>\n",
       "      <td>19.463333</td>\n",
       "      <td>36.260000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>43.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>45.090000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>39.363333</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>755.083333</td>\n",
       "      <td>99.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>-0.8330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>19.700000</td>\n",
       "      <td>38.060000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>21.066667</td>\n",
       "      <td>37.663333</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>36.200000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>39.790000</td>\n",
       "      <td>-0.533</td>\n",
       "      <td>755.700000</td>\n",
       "      <td>99.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>-0.5670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1999 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             T1       RH_1         T2       RH_2         T3       RH_3  \\\n",
       "0     19.890000  46.066667  19.200000  44.590000  19.790000  45.000000   \n",
       "1     19.890000  45.766667  19.200000  44.500000  19.790000  44.900000   \n",
       "2     19.926667  45.863333  19.356667  44.400000  19.790000  44.900000   \n",
       "3     20.066667  46.396667  19.426667  44.400000  19.790000  44.826667   \n",
       "4     20.566667  53.893333  20.033333  46.756667  20.100000  48.466667   \n",
       "5     20.856667  53.660000  20.200000  47.056667  20.200000  48.447500   \n",
       "6     20.963333  47.633333  20.260000  45.530000  20.200000  47.026667   \n",
       "7     21.033333  47.063333  20.290000  45.223333  20.260000  46.826667   \n",
       "8     21.356667  45.826667  20.666667  45.163333  20.390000  46.090000   \n",
       "9     21.600000  44.200000  21.000000  43.700000  20.500000  45.590000   \n",
       "10    21.166667  45.230000  20.500000  44.126667  20.100000  45.500000   \n",
       "11    21.000000  45.400000  20.390000  44.200000  20.100000  45.590000   \n",
       "12    20.926667  45.590000  20.290000  44.290000  20.166667  45.530000   \n",
       "13    20.700000  46.260000  20.100000  44.590000  20.133333  45.400000   \n",
       "14    20.600000  46.126667  20.000000  44.500000  20.200000  45.400000   \n",
       "15    20.500000  46.290000  19.790000  44.530000  20.290000  45.400000   \n",
       "16    20.390000  46.363333  19.700000  44.590000  20.200000  45.500000   \n",
       "17    20.290000  46.500000  19.700000  44.700000  20.260000  45.500000   \n",
       "18    20.100000  46.500000  19.426667  44.730000  20.290000  45.590000   \n",
       "19    20.033333  46.360000  19.390000  44.730000  20.290000  45.590000   \n",
       "20    19.926667  46.230000  19.290000  44.900000  20.323333  45.590000   \n",
       "21    19.890000  46.230000  19.290000  44.925000  20.323333  45.590000   \n",
       "22    19.790000  46.790000  19.200000  45.326667  20.166667  45.290000   \n",
       "23    19.790000  48.366667  19.100000  45.000000  20.100000  45.290000   \n",
       "24    19.700000  45.590000  18.890000  44.230000  19.890000  44.900000   \n",
       "25    19.890000  47.160000  19.033333  44.566667  19.926667  44.790000   \n",
       "26    20.100000  45.400000  19.200000  43.900000  20.000000  44.790000   \n",
       "27    20.166667  46.993333  19.200000  43.760000  19.890000  44.790000   \n",
       "28    20.066667  45.363333  19.100000  43.745000  19.890000  44.863333   \n",
       "29    20.000000  44.260000  19.100000  43.290000  19.890000  44.900000   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1969  19.066667  36.966667  16.856667  39.466667  19.533333  38.433333   \n",
       "1970  19.000000  36.900000  16.790000  39.400000  19.533333  38.560000   \n",
       "1971  19.000000  37.230000  16.760000  39.760000  19.600000  38.326667   \n",
       "1972  18.890000  37.500000  16.890000  39.900000  19.390000  37.463333   \n",
       "1973  18.926667  37.900000  17.100000  39.826667  19.390000  37.400000   \n",
       "1974  19.000000  37.700000  17.356667  39.433333  19.390000  37.200000   \n",
       "1975  19.356667  39.526667  17.730000  39.163333  20.360000  40.156667   \n",
       "1976  20.050000  46.400000  18.500000  40.060000  21.066667  41.426667   \n",
       "1977  20.100000  45.590000  18.566667  40.630000  21.263333  42.633333   \n",
       "1978  20.600000  41.030000  19.200000  39.230000  21.960000  39.426667   \n",
       "1979  21.245000  40.795000  19.790000  37.950000  22.890000  38.450000   \n",
       "1980  21.390000  42.626667  19.790000  38.133333  22.633333  37.933333   \n",
       "1981  21.463333  39.466667  19.963333  38.133333  23.133333  38.490000   \n",
       "1982  21.260000  38.090000  19.633333  37.530000  22.290000  36.163333   \n",
       "1983  21.000000  37.700000  19.100000  37.933333  21.760000  35.900000   \n",
       "1984  21.100000  42.423333  19.000000  38.956667  21.600000  35.963333   \n",
       "1985  21.133333  41.296667  19.100000  39.900000  21.600000  36.633333   \n",
       "1986  21.290000  39.096667  19.200000  39.930000  21.600000  36.760000   \n",
       "1987  21.290000  38.225000  19.200000  39.163333  21.463333  36.526667   \n",
       "1988  21.290000  37.933333  19.133333  39.090000  21.390000  36.400000   \n",
       "1989  21.100000  37.000000  18.790000  38.326667  21.166667  36.790000   \n",
       "1990  20.890000  37.766667  18.390000  39.966667  21.100000  36.966667   \n",
       "1991  20.790000  38.030000  18.290000  40.400000  21.033333  36.933333   \n",
       "1992  20.390000  38.400000  17.856667  40.966667  20.963333  37.090000   \n",
       "1993  20.290000  38.400000  17.790000  41.060000  20.890000  37.200000   \n",
       "1994  20.200000  38.400000  17.633333  41.126667  21.100000  37.200000   \n",
       "1995  20.100000  38.326667  17.600000  41.060000  21.100000  37.200000   \n",
       "1996  20.000000  38.200000  17.566667  41.000000  21.000000  37.230000   \n",
       "1997  19.890000  38.090000  17.390000  40.900000  21.000000  37.290000   \n",
       "1998  19.700000  38.060000  17.200000  41.000000  21.066667  37.663333   \n",
       "\n",
       "             T4       RH_4         T5       RH_5    ...             T8  \\\n",
       "0     18.890000  45.723333  17.166667  55.090000    ...      18.100000   \n",
       "1     18.890000  45.790000  17.100000  54.966667    ...      18.100000   \n",
       "2     18.890000  46.430000  17.100000  55.000000    ...      18.100000   \n",
       "3     19.000000  46.430000  17.100000  55.000000    ...      18.066667   \n",
       "4     19.000000  48.490000  17.150000  56.042500    ...      18.150000   \n",
       "5     18.890000  47.963333  17.200000  56.933333    ...      18.356667   \n",
       "6     19.260000  49.696667  17.666667  79.730000    ...      18.856667   \n",
       "7     19.323333  49.166667  17.600000  79.256667    ...      18.890000   \n",
       "8     19.390000  47.500000  18.600000  62.430000    ...      19.200000   \n",
       "9     20.996667  47.030000  19.222500  51.520000    ...      19.290000   \n",
       "10    20.460000  47.360000  19.066667  50.260000    ...      19.356667   \n",
       "11    21.263333  47.466667  19.000000  50.090000    ...      19.290000   \n",
       "12    21.890000  46.966667  19.000000  50.030000    ...      19.290000   \n",
       "13    21.230000  45.900000  18.790000  50.090000    ...      19.200000   \n",
       "14    20.745000  45.900000  18.730000  50.090000    ...      19.133333   \n",
       "15    20.390000  46.030000  18.700000  50.090000    ...      19.200000   \n",
       "16    20.230000  46.260000  18.600000  50.000000    ...      19.200000   \n",
       "17    20.100000  46.290000  18.600000  50.000000    ...      19.100000   \n",
       "18    19.700000  46.663333  18.472500  49.975000    ...      19.100000   \n",
       "19    19.666667  46.663333  18.390000  49.900000    ...      19.050000   \n",
       "20    19.500000  46.700000  18.390000  49.900000    ...      19.033333   \n",
       "21    19.500000  46.700000  18.356667  49.933333    ...      19.000000   \n",
       "22    19.290000  46.596667  18.230000  50.833333    ...      18.926667   \n",
       "23    19.166667  45.966667  18.100000  51.826667    ...      18.856667   \n",
       "24    19.166667  45.026667  18.000000  49.656667    ...      18.533333   \n",
       "25    20.066667  44.860000  18.100000  49.566667    ...      18.500000   \n",
       "26    20.000000  43.826667  17.963333  49.890000    ...      18.600000   \n",
       "27    19.890000  44.163333  17.890000  50.560000    ...      18.500000   \n",
       "28    19.790000  43.863333  17.790000  50.933333    ...      18.500000   \n",
       "29    19.700000  43.400000  17.790000  51.000000    ...      18.500000   \n",
       "...         ...        ...        ...        ...    ...            ...   \n",
       "1969  18.500000  36.500000  17.290000  55.760000    ...      20.500000   \n",
       "1970  18.390000  36.400000  17.230000  55.500000    ...      20.463333   \n",
       "1971  18.290000  36.700000  17.340000  53.690000    ...      20.390000   \n",
       "1972  18.200000  36.260000  17.726667  54.400000    ...      20.200000   \n",
       "1973  18.100000  35.950000  18.033333  52.426667    ...      20.033333   \n",
       "1974  18.100000  35.900000  17.730000  50.656667    ...      19.963333   \n",
       "1975  18.000000  35.700000  17.600000  50.545000    ...      19.790000   \n",
       "1976  18.166667  35.760000  17.500000  50.133333    ...      19.700000   \n",
       "1977  18.166667  35.626667  17.500000  50.290000    ...      19.700000   \n",
       "1978  18.200000  35.400000  17.390000  50.030000    ...      19.700000   \n",
       "1979  18.390000  35.126667  17.390000  48.900000    ...      19.890000   \n",
       "1980  18.390000  35.026667  17.390000  49.090000    ...      20.000000   \n",
       "1981  18.390000  34.290000  17.390000  49.200000    ...      20.100000   \n",
       "1982  18.290000  34.790000  17.500000  48.230000    ...      20.100000   \n",
       "1983  18.200000  35.000000  17.463333  47.430000    ...      20.600000   \n",
       "1984  18.100000  34.966667  17.390000  47.126667    ...      20.823333   \n",
       "1985  18.066667  34.930000  17.390000  47.526667    ...      20.823333   \n",
       "1986  18.000000  34.790000  17.390000  47.790000    ...      21.100000   \n",
       "1987  19.760000  35.096667  17.417500  47.617500    ...      21.100000   \n",
       "1988  19.700000  35.045000  17.390000  47.475000    ...      21.100000   \n",
       "1989  21.666667  34.760000  18.130000  68.930000    ...      20.856667   \n",
       "1990  21.000000  35.826667  18.230000  50.666667    ...      20.600000   \n",
       "1991  20.890000  36.290000  18.290000  48.696667    ...      20.500000   \n",
       "1992  21.066667  36.400000  18.200000  44.590000    ...      20.356667   \n",
       "1993  20.760000  36.290000  18.200000  44.090000    ...      20.290000   \n",
       "1994  20.230000  36.400000  18.200000  43.730000    ...      20.260000   \n",
       "1995  20.033333  36.400000  18.200000  43.590000    ...      20.200000   \n",
       "1996  19.856667  36.363333  18.133333  43.500000    ...      20.200000   \n",
       "1997  19.463333  36.260000  18.100000  43.200000    ...      20.100000   \n",
       "1998  19.200000  36.200000  18.100000  43.000000    ...      20.000000   \n",
       "\n",
       "           RH_8         T9       RH_9  T_out  Press_mm_hg     RH_out  \\\n",
       "0     48.590000  17.000000  45.400000  6.250   733.800000  92.000000   \n",
       "1     48.590000  17.000000  45.290000  5.900   734.100000  92.000000   \n",
       "2     48.590000  17.000000  45.290000  5.970   734.366667  91.333333   \n",
       "3     48.633333  16.890000  45.290000  5.980   734.433333  91.166667   \n",
       "4     49.200000  16.890000  45.326667  6.000   734.850000  89.500000   \n",
       "5     50.026667  16.890000  45.290000  6.000   735.083333  88.500000   \n",
       "6     50.290000  16.890000  45.126667  6.000   735.300000  87.500000   \n",
       "7     50.163333  16.890000  45.060000  6.000   735.333333  87.333333   \n",
       "8     49.433333  16.890000  44.790000  5.800   735.600000  87.500000   \n",
       "9     49.230000  16.890000  44.500000  5.850   736.216667  87.166667   \n",
       "10    52.090000  16.890000  46.030000  5.130   737.400000  92.000000   \n",
       "11    52.500000  16.890000  46.200000  5.120   737.633333  92.000000   \n",
       "12    53.500000  16.890000  46.290000  5.150   737.900000  92.000000   \n",
       "13    54.966667  17.000000  47.230000  5.070   738.500000  92.000000   \n",
       "14    54.790000  17.000000  47.663333  4.870   738.800000  92.000000   \n",
       "15    55.090000  17.000000  48.030000  4.830   739.133333  92.333333   \n",
       "16    54.730000  17.066667  48.363333  4.880   739.483333  92.833333   \n",
       "17    54.560000  17.033333  48.463333  4.900   739.600000  93.000000   \n",
       "18    54.230000  17.100000  49.290000  4.980   739.966667  91.666667   \n",
       "19    54.045000  17.100000  49.363333  4.970   740.033333  92.333333   \n",
       "20    53.826667  17.100000  49.700000  4.900   740.300000  95.000000   \n",
       "21    53.790000  17.100000  49.826667  4.930   740.433333  94.666667   \n",
       "22    53.800000  17.200000  49.963333  5.100   741.333333  92.333333   \n",
       "23    52.363333  17.150000  48.995000  5.100   741.683333  91.333333   \n",
       "24    49.400000  17.000000  45.730000  5.480   742.883333  90.166667   \n",
       "25    49.090000  17.033333  45.230000  6.220   743.216667  86.833333   \n",
       "26    47.352222  17.100000  44.863333  7.250   743.500000  80.000000   \n",
       "27    47.126667  17.166667  44.626667  7.500   743.500000  79.000000   \n",
       "28    47.330000  17.100000  44.400000  7.250   743.500000  80.000000   \n",
       "29    46.863333  17.100000  44.200000  6.970   743.600000  79.333333   \n",
       "...         ...        ...        ...    ...          ...        ...   \n",
       "1969  43.700000  17.666667  41.290000  0.167   747.266667  99.333333   \n",
       "1970  43.700000  17.666667  41.363333  0.200   747.300000  99.000000   \n",
       "1971  43.433333  17.633333  41.326667  0.267   747.566667  99.000000   \n",
       "1972  41.566667  17.600000  39.860000  0.633   747.833333  97.666667   \n",
       "1973  40.300000  17.533333  38.656667  0.967   748.000000  96.666667   \n",
       "1974  39.626667  17.533333  37.933333  1.300   748.200000  96.000000   \n",
       "1975  39.222500  17.500000  37.466667  1.900   748.466667  94.000000   \n",
       "1976  38.433333  17.500000  37.090000  3.120   748.766667  84.666667   \n",
       "1977  38.400000  17.500000  37.090000  3.300   748.800000  83.000000   \n",
       "1978  37.790000  17.600000  36.700000  3.970   748.933333  74.333333   \n",
       "1979  37.090000  17.700000  37.090000  5.500   749.200000  74.000000   \n",
       "1980  37.000000  17.790000  37.200000  5.070   749.266667  74.333333   \n",
       "1981  36.163333  17.790000  36.966667  3.770   749.500000  78.166667   \n",
       "1982  36.723333  17.790000  37.566667  3.180   750.333333  88.166667   \n",
       "1983  38.230000  17.790000  36.500000  1.400   751.300000  94.000000   \n",
       "1984  38.163333  17.700000  36.200000  1.930   751.633333  92.666667   \n",
       "1985  38.126667  17.600000  36.000000  2.200   751.800000  92.000000   \n",
       "1986  38.090000  17.666667  35.863333  1.780   752.216667  91.166667   \n",
       "1987  38.290000  17.600000  35.760000  1.250   752.550000  92.500000   \n",
       "1988  38.363333  17.600000  35.700000  1.100   752.633333  93.000000   \n",
       "1989  38.626667  17.500000  35.466667  0.633   753.133333  95.666667   \n",
       "1990  39.690000  17.500000  36.260000  0.433   753.533333  96.000000   \n",
       "1991  40.800000  17.500000  36.466667  0.350   753.666667  96.333333   \n",
       "1992  44.530000  17.500000  37.700000  0.800   754.300000  99.000000   \n",
       "1993  45.530000  17.500000  38.060000  1.070   754.366667  99.000000   \n",
       "1994  45.560000  17.500000  38.530000  0.650   754.500000  99.000000   \n",
       "1995  45.433333  17.500000  38.790000  0.283   754.566667  99.000000   \n",
       "1996  45.290000  17.500000  38.933333 -0.050   754.666667  99.000000   \n",
       "1997  45.090000  17.500000  39.363333 -0.750   755.083333  99.166667   \n",
       "1998  45.200000  17.500000  39.790000 -0.533   755.700000  99.666667   \n",
       "\n",
       "      Windspeed  Visibility  Tdewpoint  \n",
       "0      6.000000   51.500000     5.0000  \n",
       "1      5.000000   40.000000     4.7000  \n",
       "2      5.666667   40.000000     4.6300  \n",
       "3      5.833333   40.000000     4.6200  \n",
       "4      6.000000   40.000000     4.3500  \n",
       "5      6.000000   40.000000     4.1800  \n",
       "6      6.000000   40.000000     4.0000  \n",
       "7      6.000000   40.000000     3.9700  \n",
       "8      6.000000   40.000000     3.8000  \n",
       "9      6.833333   40.000000     3.7800  \n",
       "10     6.000000   37.000000     3.9300  \n",
       "11     5.833333   40.000000     3.9200  \n",
       "12     5.500000   40.000000     3.9500  \n",
       "13     4.666667   40.000000     3.8700  \n",
       "14     4.166667   40.000000     3.6700  \n",
       "15     4.666667   40.000000     3.6700  \n",
       "16     5.666667   40.000000     3.7700  \n",
       "17     6.000000   40.000000     3.8000  \n",
       "18     4.833333   40.000000     3.6800  \n",
       "19     4.666667   40.000000     3.7700  \n",
       "20     4.000000   40.000000     4.1000  \n",
       "21     4.166667   38.166667     4.1000  \n",
       "22     5.000000   32.666667     4.0000  \n",
       "23     5.000000   38.166667     3.8500  \n",
       "24     5.000000   30.833333     3.9500  \n",
       "25     5.000000   38.166667     4.1500  \n",
       "26     7.000000   34.000000     4.0500  \n",
       "27     7.000000   40.000000     4.1000  \n",
       "28     7.500000   40.000000     4.0000  \n",
       "29     7.000000   40.000000     3.5700  \n",
       "...         ...         ...        ...  \n",
       "1969   1.666667   22.666667     0.1000  \n",
       "1970   2.000000   18.000000     0.1000  \n",
       "1971   1.333333   29.333333     0.1670  \n",
       "1972   1.000000   39.000000     0.3330  \n",
       "1973   2.000000   48.333333     0.5000  \n",
       "1974   4.000000   63.000000     0.7000  \n",
       "1975   2.666667   39.666667     0.9670  \n",
       "1976   3.666667   38.000000     0.6830  \n",
       "1977   4.000000   40.000000     0.6000  \n",
       "1978   4.833333   40.000000    -0.2000  \n",
       "1979   3.000000   40.000000     1.2000  \n",
       "1980   3.000000   40.000000     0.8670  \n",
       "1981   2.666667   36.666667     0.3000  \n",
       "1982   1.833333   36.666667     1.3800  \n",
       "1983   1.000000   40.000000     0.6000  \n",
       "1984   1.000000   32.666667     0.8670  \n",
       "1985   1.000000   29.000000     1.0000  \n",
       "1986   1.000000   29.000000     0.5000  \n",
       "1987   1.000000   25.000000     0.2000  \n",
       "1988   1.000000   23.666667     0.1330  \n",
       "1989   1.833333   21.000000     0.0000  \n",
       "1990   1.166667   56.833333    -0.0833  \n",
       "1991   1.000000   61.666667    -0.1170  \n",
       "1992   1.000000   49.000000     0.6000  \n",
       "1993   1.000000   43.666667     0.8670  \n",
       "1994   1.000000   36.500000     0.4500  \n",
       "1995   1.000000   33.500000     0.0833  \n",
       "1996   1.000000   29.666667    -0.2330  \n",
       "1997   1.000000   17.500000    -0.8330  \n",
       "1998   1.333333   15.000000    -0.5670  \n",
       "\n",
       "[1999 rows x 24 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        50\n",
       "1        60\n",
       "2       230\n",
       "3       580\n",
       "4       100\n",
       "5        70\n",
       "6       190\n",
       "7       110\n",
       "8       100\n",
       "9        60\n",
       "10      380\n",
       "11      370\n",
       "12       50\n",
       "13       50\n",
       "14       40\n",
       "15       20\n",
       "16       50\n",
       "17       40\n",
       "18       50\n",
       "19       40\n",
       "20       50\n",
       "21       70\n",
       "22       60\n",
       "23       60\n",
       "24      500\n",
       "25       60\n",
       "26      170\n",
       "27       60\n",
       "28      140\n",
       "29       60\n",
       "       ... \n",
       "1969     50\n",
       "1970     60\n",
       "1971     80\n",
       "1972    150\n",
       "1973     60\n",
       "1974    700\n",
       "1975    270\n",
       "1976    630\n",
       "1977    410\n",
       "1978     50\n",
       "1979    180\n",
       "1980    410\n",
       "1981    220\n",
       "1982     90\n",
       "1983    190\n",
       "1984    100\n",
       "1985    120\n",
       "1986    120\n",
       "1987    100\n",
       "1988    100\n",
       "1989     90\n",
       "1990     60\n",
       "1991     50\n",
       "1992     60\n",
       "1993     50\n",
       "1994     40\n",
       "1995     20\n",
       "1996     30\n",
       "1997     50\n",
       "1998     50\n",
       "Name: Appliances, Length: 1999, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         60\n",
       "1         60\n",
       "2         50\n",
       "3         60\n",
       "4         50\n",
       "5         60\n",
       "6         60\n",
       "7         70\n",
       "8        430\n",
       "9        250\n",
       "10       100\n",
       "11        90\n",
       "12        80\n",
       "13       140\n",
       "14       120\n",
       "15       110\n",
       "16       110\n",
       "17       110\n",
       "18       100\n",
       "19       100\n",
       "20       100\n",
       "21       110\n",
       "22       400\n",
       "23       400\n",
       "24       390\n",
       "25       240\n",
       "26        60\n",
       "27        60\n",
       "28        50\n",
       "29        70\n",
       "        ... \n",
       "14773    230\n",
       "14774    220\n",
       "14775     90\n",
       "14776     70\n",
       "14777    370\n",
       "14778    280\n",
       "14779    240\n",
       "14780    250\n",
       "14781    220\n",
       "14782    190\n",
       "14783    160\n",
       "14784     80\n",
       "14785     60\n",
       "14786     60\n",
       "14787     60\n",
       "14788     70\n",
       "14789     80\n",
       "14790     80\n",
       "14791     70\n",
       "14792    100\n",
       "14793     90\n",
       "14794    100\n",
       "14795    220\n",
       "14796    180\n",
       "14797    120\n",
       "14798    110\n",
       "14799     90\n",
       "14800     90\n",
       "14801    420\n",
       "14802    430\n",
       "Name: Appliances, Length: 14803, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "             ########convert the Y_test column data to a list\n",
    "scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train=scaler.transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "\n",
    "from keras.models import Sequential\n",
    "\n",
    "\t\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "import numpy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Initializing Neural Network\n",
    "\n",
    "classifier = Sequential()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Dense(activation=\"relu\", input_dim=25, units=6, kernel_initializer=\"uniform\"))\n",
    "\n",
    "#classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 12))\n",
    "\n",
    "# Adding the second hidden layer\n",
    "\n",
    "classifier.add(Dense(activation=\"relu\", units=8, kernel_initializer=\"uniform\"))\n",
    "\n",
    "#classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))\n",
    "\n",
    "# Adding the output layer\n",
    "\n",
    "#classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'relu'))\n",
    "\n",
    "classifier.add(Dense(activation=\"relu\", units=8, kernel_initializer=\"uniform\"))\n",
    "\n",
    "classifier.add(Dense(activation=\"relu\", units=7, kernel_initializer=\"uniform\"))\n",
    "\n",
    "classifier.add(Dense(activation=\"relu\", units=6, kernel_initializer=\"uniform\"))\n",
    "\n",
    " \n",
    "\n",
    "classifier.add(Dense(activation=\"relu\", units=1, kernel_initializer=\"uniform\"))\n",
    "\n",
    "# Compiling Neural Network\n",
    "\n",
    "classifier.compile(optimizer = 'RMSprop', loss = 'mean_squared_error',metrics=['mse'])\n",
    "\n",
    "# Fitting our model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected dense_43_input to have shape (25,) but got array with shape (24,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-b2ccdb15e66a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Fitting our model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m90\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m    \u001b[1;31m#, batch_size = 64\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\project\\Anaconda3\\envs\\myenv\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m    950\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    951\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 952\u001b[1;33m             batch_size=batch_size)\n\u001b[0m\u001b[0;32m    953\u001b[0m         \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    954\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\project\\Anaconda3\\envs\\myenv\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    749\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    750\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 751\u001b[1;33m             exception_prefix='input')\n\u001b[0m\u001b[0;32m    752\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    753\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\project\\Anaconda3\\envs\\myenv\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    136\u001b[0m                             \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m                             str(data_shape))\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected dense_43_input to have shape (25,) but got array with shape (24,)"
     ]
    }
   ],
   "source": [
    "# Fitting our model\n",
    "\n",
    "classifier.fit(X_train, y_train, epochs = 90,)    #, batch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected dense_43_input to have shape (25,) but got array with shape (24,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-88c5ee2c2ec9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Predicting the Test set results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mY_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\project\\Anaconda3\\envs\\myenv\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[0;32m   1147\u001b[0m                              'argument.')\n\u001b[0;32m   1148\u001b[0m         \u001b[1;31m# Validate user data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1149\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_standardize_user_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1150\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1151\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\project\\Anaconda3\\envs\\myenv\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    749\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    750\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 751\u001b[1;33m             exception_prefix='input')\n\u001b[0m\u001b[0;32m    752\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    753\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\project\\Anaconda3\\envs\\myenv\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    136\u001b[0m                             \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m                             str(data_shape))\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected dense_43_input to have shape (25,) but got array with shape (24,)"
     ]
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "\n",
    "Y_pred = classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage error: 114.9637501511372\n"
     ]
    }
   ],
   "source": [
    "print(\"percentage error:\",(np.mean(np.abs(Y_test - Y_pred) / Y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 50.,  60., 230., ...,  30.,  50.,  50.])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 50.125267],\n",
       "       [ 60.207764],\n",
       "       [230.28099 ],\n",
       "       ...,\n",
       "       [ 30.025808],\n",
       "       [ 49.96041 ],\n",
       "       [ 49.917854]], dtype=float32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=np.abs(Y_test-Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=q/Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.50534058e-03, 1.64578883e-01, 7.82064056e-01, ...,\n",
       "        6.70842234e-01, 2.50534058e-03, 2.50534058e-03],\n",
       "       [2.04155273e-01, 3.46272786e-03, 7.38227114e-01, ...,\n",
       "        1.00692546e+00, 2.04155273e-01, 2.04155273e-01],\n",
       "       [3.60561981e+00, 2.83801651e+00, 1.22169826e-03, ...,\n",
       "        6.67603302e+00, 3.60561981e+00, 3.60561981e+00],\n",
       "       ...,\n",
       "       [3.99483833e-01, 4.99569861e-01, 8.69453007e-01, ...,\n",
       "        8.60277812e-04, 3.99483833e-01, 3.99483833e-01],\n",
       "       [7.91778564e-04, 1.67326482e-01, 7.82780821e-01, ...,\n",
       "        6.65347036e-01, 7.91778564e-04, 7.91778564e-04],\n",
       "       [1.64291382e-03, 1.68035762e-01, 7.82965851e-01, ...,\n",
       "        6.63928477e-01, 1.64291382e-03, 1.64291382e-03]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.149637501511372"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.97586927126189"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.15\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error: %.2f\" % mean_squared_error(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 1.00\n"
     ]
    }
   ],
   "source": [
    "print('R2 score: %.2f' % r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error:0.29\n"
     ]
    }
   ],
   "source": [
    "    print(\"Mean absolute error:%.2f\" % mean_absolute_error(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.15483326625012367\n",
      "RMSE: 0.39348858465033476\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "meanSquaredError=mean_squared_error(Y_test, Y_pred)\n",
    "print(\"MSE:\", meanSquaredError)\n",
    "rootMeanSquaredError = sqrt(meanSquaredError)\n",
    "print(\"RMSE:\", rootMeanSquaredError)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "#plt.scatter(df2['datetime'][0:50], Y_test[0:50], s=20, edgecolor=\"black\",c=\"darkorange\", label=\"actual load\")\n",
    "\n",
    "plt.plot(df2['date'][0:50], Y_pred[0:50], color=\"#cc6699\", label=\"predicted\", linewidth=2)\n",
    "\n",
    "plt.plot(df2['date'][0:50], Y_test[0:50], color=\"black\", label=\"actual\", linewidth=2)\n",
    "\n",
    " \n",
    "\n",
    "plt.xlabel(\"time\")\n",
    "\n",
    "plt.ylabel(\"load\")\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.title(\"DNN\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}