Honeywell_Dataset_1_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Power Quality Classification Using ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "kwQRS74SMZot"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Pre-Processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "r6Mpe6gpNtRH"
   },
   "outputs": [],
   "source": [
    "col_names=[i for i in range(128)]\n",
    "data1=pd.read_csv('Voltage_L1_DataSet1.csv',names=col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "id": "B0ln9kYpTXxI",
    "outputId": "bacd0222-4eb1-4a61-c27d-4bfe03acc496"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2464.190673</td>\n",
       "      <td>-2925.256244</td>\n",
       "      <td>-3358.931979</td>\n",
       "      <td>-3760.652243</td>\n",
       "      <td>-4124.939861</td>\n",
       "      <td>-4449.055880</td>\n",
       "      <td>-4732.086714</td>\n",
       "      <td>-4971.292771</td>\n",
       "      <td>-5158.457976</td>\n",
       "      <td>-5299.060129</td>\n",
       "      <td>...</td>\n",
       "      <td>2644.052851</td>\n",
       "      <td>2168.379577</td>\n",
       "      <td>1671.705859</td>\n",
       "      <td>1157.686541</td>\n",
       "      <td>634.536384</td>\n",
       "      <td>104.083017</td>\n",
       "      <td>-427.284371</td>\n",
       "      <td>-954.999435</td>\n",
       "      <td>-1472.671036</td>\n",
       "      <td>-1976.648032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-797.962914</td>\n",
       "      <td>-1320.199586</td>\n",
       "      <td>-1828.741445</td>\n",
       "      <td>-2319.935857</td>\n",
       "      <td>-2789.218649</td>\n",
       "      <td>-3231.111864</td>\n",
       "      <td>-3641.962908</td>\n",
       "      <td>-4018.119193</td>\n",
       "      <td>-4355.929647</td>\n",
       "      <td>-4650.829055</td>\n",
       "      <td>...</td>\n",
       "      <td>4008.077109</td>\n",
       "      <td>3632.834002</td>\n",
       "      <td>3221.982426</td>\n",
       "      <td>2780.090228</td>\n",
       "      <td>2311.720161</td>\n",
       "      <td>1821.438861</td>\n",
       "      <td>1311.983509</td>\n",
       "      <td>790.659456</td>\n",
       "      <td>262.031836</td>\n",
       "      <td>-269.335205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4387.885674</td>\n",
       "      <td>4567.747164</td>\n",
       "      <td>4693.741356</td>\n",
       "      <td>4776.824895</td>\n",
       "      <td>4824.300958</td>\n",
       "      <td>4850.777519</td>\n",
       "      <td>4862.646175</td>\n",
       "      <td>4867.211521</td>\n",
       "      <td>4866.298624</td>\n",
       "      <td>4858.081691</td>\n",
       "      <td>...</td>\n",
       "      <td>-850.916937</td>\n",
       "      <td>-165.252287</td>\n",
       "      <td>524.062959</td>\n",
       "      <td>1199.684611</td>\n",
       "      <td>1842.437384</td>\n",
       "      <td>2437.714882</td>\n",
       "      <td>2972.733259</td>\n",
       "      <td>3437.450839</td>\n",
       "      <td>3828.215741</td>\n",
       "      <td>4143.201102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4050.074299</td>\n",
       "      <td>-4340.408587</td>\n",
       "      <td>-4576.875995</td>\n",
       "      <td>-4769.518913</td>\n",
       "      <td>-4918.338175</td>\n",
       "      <td>-5025.159834</td>\n",
       "      <td>-5100.026323</td>\n",
       "      <td>-5141.111052</td>\n",
       "      <td>-5153.892968</td>\n",
       "      <td>-5137.458861</td>\n",
       "      <td>...</td>\n",
       "      <td>1161.338395</td>\n",
       "      <td>563.322919</td>\n",
       "      <td>-47.475988</td>\n",
       "      <td>-656.447463</td>\n",
       "      <td>-1252.637741</td>\n",
       "      <td>-1825.089177</td>\n",
       "      <td>-2364.673580</td>\n",
       "      <td>-2861.346591</td>\n",
       "      <td>-3309.630078</td>\n",
       "      <td>-3706.785897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3458.450134</td>\n",
       "      <td>3686.700705</td>\n",
       "      <td>4000.773209</td>\n",
       "      <td>4392.451258</td>\n",
       "      <td>4834.343975</td>\n",
       "      <td>5269.845213</td>\n",
       "      <td>5636.871457</td>\n",
       "      <td>5876.078192</td>\n",
       "      <td>5950.944421</td>\n",
       "      <td>5841.384319</td>\n",
       "      <td>...</td>\n",
       "      <td>-1450.758930</td>\n",
       "      <td>-722.183210</td>\n",
       "      <td>67.562203</td>\n",
       "      <td>850.005032</td>\n",
       "      <td>1561.232918</td>\n",
       "      <td>2152.858410</td>\n",
       "      <td>2601.141643</td>\n",
       "      <td>2912.474970</td>\n",
       "      <td>3123.378574</td>\n",
       "      <td>3286.805684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0            1            2            3            4    \\\n",
       "0 -2464.190673 -2925.256244 -3358.931979 -3760.652243 -4124.939861   \n",
       "1  -797.962914 -1320.199586 -1828.741445 -2319.935857 -2789.218649   \n",
       "2  4387.885674  4567.747164  4693.741356  4776.824895  4824.300958   \n",
       "3 -4050.074299 -4340.408587 -4576.875995 -4769.518913 -4918.338175   \n",
       "4  3458.450134  3686.700705  4000.773209  4392.451258  4834.343975   \n",
       "\n",
       "           5            6            7            8            9    ...  \\\n",
       "0 -4449.055880 -4732.086714 -4971.292771 -5158.457976 -5299.060129  ...   \n",
       "1 -3231.111864 -3641.962908 -4018.119193 -4355.929647 -4650.829055  ...   \n",
       "2  4850.777519  4862.646175  4867.211521  4866.298624  4858.081691  ...   \n",
       "3 -5025.159834 -5100.026323 -5141.111052 -5153.892968 -5137.458861  ...   \n",
       "4  5269.845213  5636.871457  5876.078192  5950.944421  5841.384319  ...   \n",
       "\n",
       "           118          119          120          121          122  \\\n",
       "0  2644.052851  2168.379577  1671.705859  1157.686541   634.536384   \n",
       "1  4008.077109  3632.834002  3221.982426  2780.090228  2311.720161   \n",
       "2  -850.916937  -165.252287   524.062959  1199.684611  1842.437384   \n",
       "3  1161.338395   563.322919   -47.475988  -656.447463 -1252.637741   \n",
       "4 -1450.758930  -722.183210    67.562203   850.005032  1561.232918   \n",
       "\n",
       "           123          124          125          126          127  \n",
       "0   104.083017  -427.284371  -954.999435 -1472.671036 -1976.648032  \n",
       "1  1821.438861  1311.983509   790.659456   262.031836  -269.335205  \n",
       "2  2437.714882  2972.733259  3437.450839  3828.215741  4143.201102  \n",
       "3 -1825.089177 -2364.673580 -2861.346591 -3309.630078 -3706.785897  \n",
       "4  2152.858410  2601.141643  2912.474970  3123.378574  3286.805684  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treating Null Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "mN5QY6yKTaze",
    "outputId": "d618376c-a371-4372-d5ff-fc7550542eca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11895</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11897</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11898</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11899</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9    ...  118  119  120  \\\n",
       "11895  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "11896  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "11897  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "11898  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "11899  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "\n",
       "       121  122  123  124  125  126  127  \n",
       "11895  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "11896  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "11897  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "11898  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "11899  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5900\n",
       "1      5900\n",
       "2      5900\n",
       "3      5900\n",
       "4      5900\n",
       "       ... \n",
       "123    5900\n",
       "124    5900\n",
       "125    5900\n",
       "126    5900\n",
       "127    5900\n",
       "Length: 128, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7GgjBww8Tdgc",
    "outputId": "b7cc8ac0-7ac6-48eb-d58f-42a5094d9672"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 128)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data1.dropna()\n",
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "123    0\n",
       "124    0\n",
       "125    0\n",
       "126    0\n",
       "127    0\n",
       "Length: 128, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "id": "NF7TAg8oTzxv",
    "outputId": "ebd9a3c2-eb9b-42c3-d965-4ff8e360e089"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>...</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2464.190673</td>\n",
       "      <td>-2925.256244</td>\n",
       "      <td>-3358.931979</td>\n",
       "      <td>-3760.652243</td>\n",
       "      <td>-4124.939861</td>\n",
       "      <td>-4449.055880</td>\n",
       "      <td>-4732.086714</td>\n",
       "      <td>-4971.292771</td>\n",
       "      <td>-5158.457976</td>\n",
       "      <td>-5299.060129</td>\n",
       "      <td>-5387.621664</td>\n",
       "      <td>-5425.054710</td>\n",
       "      <td>-5409.533070</td>\n",
       "      <td>-5341.971839</td>\n",
       "      <td>-5223.281201</td>\n",
       "      <td>-5054.376368</td>\n",
       "      <td>-4836.168369</td>\n",
       "      <td>-4573.223588</td>\n",
       "      <td>-4266.455769</td>\n",
       "      <td>-3915.863360</td>\n",
       "      <td>-3530.576674</td>\n",
       "      <td>-3109.682461</td>\n",
       "      <td>-2659.573315</td>\n",
       "      <td>-2183.899008</td>\n",
       "      <td>-1687.226364</td>\n",
       "      <td>-1174.119830</td>\n",
       "      <td>-650.056949</td>\n",
       "      <td>-119.602433</td>\n",
       "      <td>411.763916</td>\n",
       "      <td>938.566132</td>\n",
       "      <td>1457.150781</td>\n",
       "      <td>1962.040853</td>\n",
       "      <td>2446.844691</td>\n",
       "      <td>2908.823187</td>\n",
       "      <td>3342.499279</td>\n",
       "      <td>3743.306206</td>\n",
       "      <td>4108.506856</td>\n",
       "      <td>4433.536272</td>\n",
       "      <td>4716.566376</td>\n",
       "      <td>4955.772680</td>\n",
       "      <td>...</td>\n",
       "      <td>-1689.051919</td>\n",
       "      <td>-1175.032262</td>\n",
       "      <td>-651.882744</td>\n",
       "      <td>-120.516066</td>\n",
       "      <td>410.851403</td>\n",
       "      <td>937.653413</td>\n",
       "      <td>1456.238399</td>\n",
       "      <td>1960.214696</td>\n",
       "      <td>2445.931971</td>\n",
       "      <td>2907.910689</td>\n",
       "      <td>3341.585709</td>\n",
       "      <td>3743.306544</td>\n",
       "      <td>4107.594715</td>\n",
       "      <td>4432.622864</td>\n",
       "      <td>4715.653438</td>\n",
       "      <td>4954.859274</td>\n",
       "      <td>5142.025125</td>\n",
       "      <td>5282.626834</td>\n",
       "      <td>5372.101699</td>\n",
       "      <td>5408.621318</td>\n",
       "      <td>5394.013731</td>\n",
       "      <td>5326.451254</td>\n",
       "      <td>5207.761351</td>\n",
       "      <td>5037.943127</td>\n",
       "      <td>4819.735896</td>\n",
       "      <td>4557.703784</td>\n",
       "      <td>4250.935882</td>\n",
       "      <td>3899.430274</td>\n",
       "      <td>3514.143809</td>\n",
       "      <td>3093.249847</td>\n",
       "      <td>2644.052851</td>\n",
       "      <td>2168.379577</td>\n",
       "      <td>1671.705859</td>\n",
       "      <td>1157.686541</td>\n",
       "      <td>634.536384</td>\n",
       "      <td>104.083017</td>\n",
       "      <td>-427.284371</td>\n",
       "      <td>-954.999435</td>\n",
       "      <td>-1472.671036</td>\n",
       "      <td>-1976.648032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-797.962914</td>\n",
       "      <td>-1320.199586</td>\n",
       "      <td>-1828.741445</td>\n",
       "      <td>-2319.935857</td>\n",
       "      <td>-2789.218649</td>\n",
       "      <td>-3231.111864</td>\n",
       "      <td>-3641.962908</td>\n",
       "      <td>-4018.119193</td>\n",
       "      <td>-4355.929647</td>\n",
       "      <td>-4650.829055</td>\n",
       "      <td>-4904.643864</td>\n",
       "      <td>-5107.330184</td>\n",
       "      <td>-5261.626825</td>\n",
       "      <td>-5366.622879</td>\n",
       "      <td>-5418.663525</td>\n",
       "      <td>-5419.576973</td>\n",
       "      <td>-5367.535249</td>\n",
       "      <td>-5264.365732</td>\n",
       "      <td>-5110.069166</td>\n",
       "      <td>-4906.470001</td>\n",
       "      <td>-4656.307539</td>\n",
       "      <td>-4364.146538</td>\n",
       "      <td>-4024.510575</td>\n",
       "      <td>-3650.179436</td>\n",
       "      <td>-3239.328934</td>\n",
       "      <td>-2797.436518</td>\n",
       "      <td>-2329.066729</td>\n",
       "      <td>-1837.871278</td>\n",
       "      <td>-1329.329968</td>\n",
       "      <td>-808.005760</td>\n",
       "      <td>-278.465093</td>\n",
       "      <td>252.902023</td>\n",
       "      <td>782.442935</td>\n",
       "      <td>1303.766529</td>\n",
       "      <td>1812.308746</td>\n",
       "      <td>2303.502822</td>\n",
       "      <td>2772.785644</td>\n",
       "      <td>3215.592255</td>\n",
       "      <td>3626.442570</td>\n",
       "      <td>4002.599102</td>\n",
       "      <td>...</td>\n",
       "      <td>-3239.328487</td>\n",
       "      <td>-2797.435948</td>\n",
       "      <td>-2329.066521</td>\n",
       "      <td>-1837.871910</td>\n",
       "      <td>-1329.329479</td>\n",
       "      <td>-808.005477</td>\n",
       "      <td>-279.377475</td>\n",
       "      <td>252.901869</td>\n",
       "      <td>781.530215</td>\n",
       "      <td>1303.767033</td>\n",
       "      <td>1812.308177</td>\n",
       "      <td>2304.416161</td>\n",
       "      <td>2773.699506</td>\n",
       "      <td>3215.591849</td>\n",
       "      <td>3627.355635</td>\n",
       "      <td>4002.598697</td>\n",
       "      <td>4340.409797</td>\n",
       "      <td>4635.308762</td>\n",
       "      <td>4889.123899</td>\n",
       "      <td>5091.809794</td>\n",
       "      <td>5246.107486</td>\n",
       "      <td>5351.102295</td>\n",
       "      <td>5403.143675</td>\n",
       "      <td>5403.143732</td>\n",
       "      <td>5351.102775</td>\n",
       "      <td>5247.932927</td>\n",
       "      <td>5093.636278</td>\n",
       "      <td>4889.123913</td>\n",
       "      <td>4639.874673</td>\n",
       "      <td>4346.800923</td>\n",
       "      <td>4008.077109</td>\n",
       "      <td>3632.834002</td>\n",
       "      <td>3221.982426</td>\n",
       "      <td>2780.090228</td>\n",
       "      <td>2311.720161</td>\n",
       "      <td>1821.438861</td>\n",
       "      <td>1311.983509</td>\n",
       "      <td>790.659456</td>\n",
       "      <td>262.031836</td>\n",
       "      <td>-269.335205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4387.885674</td>\n",
       "      <td>4567.747164</td>\n",
       "      <td>4693.741356</td>\n",
       "      <td>4776.824895</td>\n",
       "      <td>4824.300958</td>\n",
       "      <td>4850.777519</td>\n",
       "      <td>4862.646175</td>\n",
       "      <td>4867.211521</td>\n",
       "      <td>4866.298624</td>\n",
       "      <td>4858.081691</td>\n",
       "      <td>4839.821272</td>\n",
       "      <td>4801.475225</td>\n",
       "      <td>4735.739730</td>\n",
       "      <td>4633.482680</td>\n",
       "      <td>4480.098867</td>\n",
       "      <td>4263.717062</td>\n",
       "      <td>3982.513234</td>\n",
       "      <td>3625.529989</td>\n",
       "      <td>3194.592586</td>\n",
       "      <td>2690.615580</td>\n",
       "      <td>2121.815686</td>\n",
       "      <td>1499.149171</td>\n",
       "      <td>836.309474</td>\n",
       "      <td>150.645858</td>\n",
       "      <td>-539.583464</td>\n",
       "      <td>-1214.291896</td>\n",
       "      <td>-1857.957949</td>\n",
       "      <td>-2453.234297</td>\n",
       "      <td>-2987.340713</td>\n",
       "      <td>-3452.058138</td>\n",
       "      <td>-3842.822995</td>\n",
       "      <td>-4158.721282</td>\n",
       "      <td>-4403.405653</td>\n",
       "      <td>-4583.267220</td>\n",
       "      <td>-4709.261054</td>\n",
       "      <td>-4793.257930</td>\n",
       "      <td>-4839.820962</td>\n",
       "      <td>-4866.297128</td>\n",
       "      <td>-4879.079514</td>\n",
       "      <td>-4882.731612</td>\n",
       "      <td>...</td>\n",
       "      <td>-542.322021</td>\n",
       "      <td>-1217.030331</td>\n",
       "      <td>-1860.696745</td>\n",
       "      <td>-2455.060932</td>\n",
       "      <td>-2989.166228</td>\n",
       "      <td>-3453.883859</td>\n",
       "      <td>-3844.648379</td>\n",
       "      <td>-4159.634438</td>\n",
       "      <td>-4404.318373</td>\n",
       "      <td>-4583.266716</td>\n",
       "      <td>-4709.261622</td>\n",
       "      <td>-4793.257592</td>\n",
       "      <td>-4839.820102</td>\n",
       "      <td>-4866.297534</td>\n",
       "      <td>-4879.079451</td>\n",
       "      <td>-4882.732017</td>\n",
       "      <td>-4881.818474</td>\n",
       "      <td>-4873.601984</td>\n",
       "      <td>-4855.341237</td>\n",
       "      <td>-4817.908616</td>\n",
       "      <td>-4751.259069</td>\n",
       "      <td>-4649.003265</td>\n",
       "      <td>-4496.531718</td>\n",
       "      <td>-4279.237302</td>\n",
       "      <td>-3998.032706</td>\n",
       "      <td>-3641.049793</td>\n",
       "      <td>-3210.112473</td>\n",
       "      <td>-2706.135664</td>\n",
       "      <td>-2137.335549</td>\n",
       "      <td>-1514.668783</td>\n",
       "      <td>-850.916937</td>\n",
       "      <td>-165.252287</td>\n",
       "      <td>524.062959</td>\n",
       "      <td>1199.684611</td>\n",
       "      <td>1842.437384</td>\n",
       "      <td>2437.714882</td>\n",
       "      <td>2972.733259</td>\n",
       "      <td>3437.450839</td>\n",
       "      <td>3828.215741</td>\n",
       "      <td>4143.201102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4050.074299</td>\n",
       "      <td>-4340.408587</td>\n",
       "      <td>-4576.875995</td>\n",
       "      <td>-4769.518913</td>\n",
       "      <td>-4918.338175</td>\n",
       "      <td>-5025.159834</td>\n",
       "      <td>-5100.026323</td>\n",
       "      <td>-5141.111052</td>\n",
       "      <td>-5153.892968</td>\n",
       "      <td>-5137.458861</td>\n",
       "      <td>-5091.809174</td>\n",
       "      <td>-5014.204029</td>\n",
       "      <td>-4900.078226</td>\n",
       "      <td>-4746.694853</td>\n",
       "      <td>-4552.225090</td>\n",
       "      <td>-4306.628130</td>\n",
       "      <td>-4008.988999</td>\n",
       "      <td>-3659.309075</td>\n",
       "      <td>-3255.763095</td>\n",
       "      <td>-2801.088514</td>\n",
       "      <td>-2298.024633</td>\n",
       "      <td>-1753.875216</td>\n",
       "      <td>-1176.858860</td>\n",
       "      <td>-577.929348</td>\n",
       "      <td>31.955483</td>\n",
       "      <td>640.927176</td>\n",
       "      <td>1237.117177</td>\n",
       "      <td>1809.569762</td>\n",
       "      <td>2349.153124</td>\n",
       "      <td>2845.826291</td>\n",
       "      <td>3295.022824</td>\n",
       "      <td>3691.265717</td>\n",
       "      <td>4034.554320</td>\n",
       "      <td>4323.975530</td>\n",
       "      <td>4561.356298</td>\n",
       "      <td>4753.998880</td>\n",
       "      <td>4902.818171</td>\n",
       "      <td>5009.640226</td>\n",
       "      <td>5084.505985</td>\n",
       "      <td>5125.590961</td>\n",
       "      <td>...</td>\n",
       "      <td>30.129927</td>\n",
       "      <td>640.014744</td>\n",
       "      <td>1236.204383</td>\n",
       "      <td>1808.656128</td>\n",
       "      <td>2348.240611</td>\n",
       "      <td>2844.913572</td>\n",
       "      <td>3294.110443</td>\n",
       "      <td>3691.265562</td>\n",
       "      <td>4033.641600</td>\n",
       "      <td>4323.976034</td>\n",
       "      <td>4561.355729</td>\n",
       "      <td>4753.999218</td>\n",
       "      <td>4902.819032</td>\n",
       "      <td>5009.639820</td>\n",
       "      <td>5083.593047</td>\n",
       "      <td>5125.590557</td>\n",
       "      <td>5138.373119</td>\n",
       "      <td>5121.938568</td>\n",
       "      <td>5076.289209</td>\n",
       "      <td>4998.683639</td>\n",
       "      <td>4884.558887</td>\n",
       "      <td>4730.261267</td>\n",
       "      <td>4536.705240</td>\n",
       "      <td>4291.107890</td>\n",
       "      <td>3993.469527</td>\n",
       "      <td>3643.789270</td>\n",
       "      <td>3240.243209</td>\n",
       "      <td>2784.655428</td>\n",
       "      <td>2281.591768</td>\n",
       "      <td>1738.355604</td>\n",
       "      <td>1161.338395</td>\n",
       "      <td>563.322919</td>\n",
       "      <td>-47.475988</td>\n",
       "      <td>-656.447463</td>\n",
       "      <td>-1252.637741</td>\n",
       "      <td>-1825.089177</td>\n",
       "      <td>-2364.673580</td>\n",
       "      <td>-2861.346591</td>\n",
       "      <td>-3309.630078</td>\n",
       "      <td>-3706.785897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3458.450134</td>\n",
       "      <td>3686.700705</td>\n",
       "      <td>4000.773209</td>\n",
       "      <td>4392.451258</td>\n",
       "      <td>4834.343975</td>\n",
       "      <td>5269.845213</td>\n",
       "      <td>5636.871457</td>\n",
       "      <td>5876.078192</td>\n",
       "      <td>5950.944421</td>\n",
       "      <td>5841.384319</td>\n",
       "      <td>5574.787489</td>\n",
       "      <td>5187.674864</td>\n",
       "      <td>4745.782747</td>\n",
       "      <td>4310.280145</td>\n",
       "      <td>3932.297960</td>\n",
       "      <td>3637.398025</td>\n",
       "      <td>3422.843307</td>\n",
       "      <td>3256.677378</td>\n",
       "      <td>3089.597412</td>\n",
       "      <td>2864.998869</td>\n",
       "      <td>2526.275355</td>\n",
       "      <td>2051.515086</td>\n",
       "      <td>1433.412463</td>\n",
       "      <td>704.837776</td>\n",
       "      <td>-84.908711</td>\n",
       "      <td>-866.438320</td>\n",
       "      <td>-1578.579486</td>\n",
       "      <td>-2169.290827</td>\n",
       "      <td>-2616.662099</td>\n",
       "      <td>-2928.908272</td>\n",
       "      <td>-3138.898830</td>\n",
       "      <td>-3303.238866</td>\n",
       "      <td>-3473.970114</td>\n",
       "      <td>-3702.220761</td>\n",
       "      <td>-4015.379905</td>\n",
       "      <td>-4406.145289</td>\n",
       "      <td>-4848.037975</td>\n",
       "      <td>-5283.538819</td>\n",
       "      <td>-5651.478793</td>\n",
       "      <td>-5891.598282</td>\n",
       "      <td>...</td>\n",
       "      <td>-83.995262</td>\n",
       "      <td>-865.524749</td>\n",
       "      <td>-1577.666277</td>\n",
       "      <td>-2169.291459</td>\n",
       "      <td>-2617.574612</td>\n",
       "      <td>-2928.907989</td>\n",
       "      <td>-3138.898210</td>\n",
       "      <td>-3303.239020</td>\n",
       "      <td>-3473.969832</td>\n",
       "      <td>-3702.220257</td>\n",
       "      <td>-4015.380473</td>\n",
       "      <td>-4407.057953</td>\n",
       "      <td>-4848.037115</td>\n",
       "      <td>-5283.539225</td>\n",
       "      <td>-5650.565728</td>\n",
       "      <td>-5890.685686</td>\n",
       "      <td>-5966.464270</td>\n",
       "      <td>-5857.817614</td>\n",
       "      <td>-5592.133458</td>\n",
       "      <td>-5205.021257</td>\n",
       "      <td>-4764.041090</td>\n",
       "      <td>-4328.539734</td>\n",
       "      <td>-3949.643813</td>\n",
       "      <td>-3653.831266</td>\n",
       "      <td>-3439.275781</td>\n",
       "      <td>-3272.197182</td>\n",
       "      <td>-3106.030300</td>\n",
       "      <td>-2881.431954</td>\n",
       "      <td>-2542.708221</td>\n",
       "      <td>-2067.947699</td>\n",
       "      <td>-1450.758930</td>\n",
       "      <td>-722.183210</td>\n",
       "      <td>67.562203</td>\n",
       "      <td>850.005032</td>\n",
       "      <td>1561.232918</td>\n",
       "      <td>2152.858410</td>\n",
       "      <td>2601.141643</td>\n",
       "      <td>2912.474970</td>\n",
       "      <td>3123.378574</td>\n",
       "      <td>3286.805684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0            1            2    ...          125          126          127\n",
       "0 -2464.190673 -2925.256244 -3358.931979  ...  -954.999435 -1472.671036 -1976.648032\n",
       "1  -797.962914 -1320.199586 -1828.741445  ...   790.659456   262.031836  -269.335205\n",
       "2  4387.885674  4567.747164  4693.741356  ...  3437.450839  3828.215741  4143.201102\n",
       "3 -4050.074299 -4340.408587 -4576.875995  ... -2861.346591 -3309.630078 -3706.785897\n",
       "4  3458.450134  3686.700705  4000.773209  ...  2912.474970  3123.378574  3286.805684\n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding Correlation between Data Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "WoWEtq1bUBnK",
    "outputId": "a89e3e4f-33f8-4c52-e8c0-ece3cdb988da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(data1.corr(),vmin=-1, vmax=1, cmap='BrBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "drh0S-9zUcre",
    "outputId": "e306684b-e5f2-4fc1-d246-eee9d4df4185"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>...</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990863</td>\n",
       "      <td>0.976753</td>\n",
       "      <td>0.952708</td>\n",
       "      <td>0.920379</td>\n",
       "      <td>0.879712</td>\n",
       "      <td>0.831326</td>\n",
       "      <td>0.774718</td>\n",
       "      <td>0.710848</td>\n",
       "      <td>0.641221</td>\n",
       "      <td>0.563950</td>\n",
       "      <td>0.482395</td>\n",
       "      <td>0.396321</td>\n",
       "      <td>0.304921</td>\n",
       "      <td>0.210685</td>\n",
       "      <td>0.114079</td>\n",
       "      <td>0.017626</td>\n",
       "      <td>-0.079939</td>\n",
       "      <td>-0.177465</td>\n",
       "      <td>-0.271803</td>\n",
       "      <td>-0.363844</td>\n",
       "      <td>-0.452042</td>\n",
       "      <td>-0.536378</td>\n",
       "      <td>-0.613583</td>\n",
       "      <td>-0.683791</td>\n",
       "      <td>-0.750389</td>\n",
       "      <td>-0.806317</td>\n",
       "      <td>-0.856358</td>\n",
       "      <td>-0.897494</td>\n",
       "      <td>-0.929411</td>\n",
       "      <td>-0.952545</td>\n",
       "      <td>-0.967306</td>\n",
       "      <td>-0.973084</td>\n",
       "      <td>-0.968272</td>\n",
       "      <td>-0.953993</td>\n",
       "      <td>-0.929271</td>\n",
       "      <td>-0.898328</td>\n",
       "      <td>-0.857996</td>\n",
       "      <td>-0.810434</td>\n",
       "      <td>-0.755719</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.663052</td>\n",
       "      <td>-0.724004</td>\n",
       "      <td>-0.779649</td>\n",
       "      <td>-0.826415</td>\n",
       "      <td>-0.864802</td>\n",
       "      <td>-0.895810</td>\n",
       "      <td>-0.918332</td>\n",
       "      <td>-0.931675</td>\n",
       "      <td>-0.935248</td>\n",
       "      <td>-0.930265</td>\n",
       "      <td>-0.916108</td>\n",
       "      <td>-0.892867</td>\n",
       "      <td>-0.861835</td>\n",
       "      <td>-0.821667</td>\n",
       "      <td>-0.775726</td>\n",
       "      <td>-0.722173</td>\n",
       "      <td>-0.659865</td>\n",
       "      <td>-0.594229</td>\n",
       "      <td>-0.517866</td>\n",
       "      <td>-0.441188</td>\n",
       "      <td>-0.358183</td>\n",
       "      <td>-0.270014</td>\n",
       "      <td>-0.180594</td>\n",
       "      <td>-0.087902</td>\n",
       "      <td>0.005088</td>\n",
       "      <td>0.098872</td>\n",
       "      <td>0.192891</td>\n",
       "      <td>0.285414</td>\n",
       "      <td>0.374086</td>\n",
       "      <td>0.458829</td>\n",
       "      <td>0.539911</td>\n",
       "      <td>0.614397</td>\n",
       "      <td>0.682506</td>\n",
       "      <td>0.744624</td>\n",
       "      <td>0.799977</td>\n",
       "      <td>0.847121</td>\n",
       "      <td>0.887535</td>\n",
       "      <td>0.919003</td>\n",
       "      <td>0.941647</td>\n",
       "      <td>0.955655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.990863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990991</td>\n",
       "      <td>0.975942</td>\n",
       "      <td>0.951701</td>\n",
       "      <td>0.919386</td>\n",
       "      <td>0.878402</td>\n",
       "      <td>0.830573</td>\n",
       "      <td>0.772742</td>\n",
       "      <td>0.709230</td>\n",
       "      <td>0.637652</td>\n",
       "      <td>0.561183</td>\n",
       "      <td>0.479713</td>\n",
       "      <td>0.392092</td>\n",
       "      <td>0.300790</td>\n",
       "      <td>0.206273</td>\n",
       "      <td>0.110908</td>\n",
       "      <td>0.013392</td>\n",
       "      <td>-0.085039</td>\n",
       "      <td>-0.181280</td>\n",
       "      <td>-0.276196</td>\n",
       "      <td>-0.368026</td>\n",
       "      <td>-0.456880</td>\n",
       "      <td>-0.539206</td>\n",
       "      <td>-0.615100</td>\n",
       "      <td>-0.687995</td>\n",
       "      <td>-0.750624</td>\n",
       "      <td>-0.808058</td>\n",
       "      <td>-0.856885</td>\n",
       "      <td>-0.897048</td>\n",
       "      <td>-0.928870</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.967769</td>\n",
       "      <td>-0.972263</td>\n",
       "      <td>-0.967303</td>\n",
       "      <td>-0.952206</td>\n",
       "      <td>-0.928651</td>\n",
       "      <td>-0.896847</td>\n",
       "      <td>-0.856867</td>\n",
       "      <td>-0.809352</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.597513</td>\n",
       "      <td>-0.664403</td>\n",
       "      <td>-0.726561</td>\n",
       "      <td>-0.780281</td>\n",
       "      <td>-0.825947</td>\n",
       "      <td>-0.865029</td>\n",
       "      <td>-0.896027</td>\n",
       "      <td>-0.918093</td>\n",
       "      <td>-0.930296</td>\n",
       "      <td>-0.934464</td>\n",
       "      <td>-0.929270</td>\n",
       "      <td>-0.914782</td>\n",
       "      <td>-0.892055</td>\n",
       "      <td>-0.859879</td>\n",
       "      <td>-0.821345</td>\n",
       "      <td>-0.774917</td>\n",
       "      <td>-0.719743</td>\n",
       "      <td>-0.658948</td>\n",
       "      <td>-0.589948</td>\n",
       "      <td>-0.516341</td>\n",
       "      <td>-0.438293</td>\n",
       "      <td>-0.353967</td>\n",
       "      <td>-0.266883</td>\n",
       "      <td>-0.176655</td>\n",
       "      <td>-0.084784</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.103622</td>\n",
       "      <td>0.197966</td>\n",
       "      <td>0.289218</td>\n",
       "      <td>0.377962</td>\n",
       "      <td>0.463024</td>\n",
       "      <td>0.542287</td>\n",
       "      <td>0.616492</td>\n",
       "      <td>0.684140</td>\n",
       "      <td>0.745961</td>\n",
       "      <td>0.800081</td>\n",
       "      <td>0.848119</td>\n",
       "      <td>0.887529</td>\n",
       "      <td>0.918357</td>\n",
       "      <td>0.941725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.976753</td>\n",
       "      <td>0.990991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991563</td>\n",
       "      <td>0.976653</td>\n",
       "      <td>0.952749</td>\n",
       "      <td>0.920103</td>\n",
       "      <td>0.879535</td>\n",
       "      <td>0.830471</td>\n",
       "      <td>0.773321</td>\n",
       "      <td>0.708152</td>\n",
       "      <td>0.637333</td>\n",
       "      <td>0.561063</td>\n",
       "      <td>0.477936</td>\n",
       "      <td>0.390304</td>\n",
       "      <td>0.298711</td>\n",
       "      <td>0.205282</td>\n",
       "      <td>0.108692</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>-0.087142</td>\n",
       "      <td>-0.184241</td>\n",
       "      <td>-0.279085</td>\n",
       "      <td>-0.371965</td>\n",
       "      <td>-0.458985</td>\n",
       "      <td>-0.540232</td>\n",
       "      <td>-0.619093</td>\n",
       "      <td>-0.688134</td>\n",
       "      <td>-0.752686</td>\n",
       "      <td>-0.808878</td>\n",
       "      <td>-0.857048</td>\n",
       "      <td>-0.897362</td>\n",
       "      <td>-0.930135</td>\n",
       "      <td>-0.954498</td>\n",
       "      <td>-0.968397</td>\n",
       "      <td>-0.973087</td>\n",
       "      <td>-0.967364</td>\n",
       "      <td>-0.953090</td>\n",
       "      <td>-0.929557</td>\n",
       "      <td>-0.897950</td>\n",
       "      <td>-0.858180</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.526123</td>\n",
       "      <td>-0.598634</td>\n",
       "      <td>-0.667004</td>\n",
       "      <td>-0.727352</td>\n",
       "      <td>-0.780217</td>\n",
       "      <td>-0.826925</td>\n",
       "      <td>-0.866185</td>\n",
       "      <td>-0.896727</td>\n",
       "      <td>-0.918058</td>\n",
       "      <td>-0.931050</td>\n",
       "      <td>-0.935116</td>\n",
       "      <td>-0.929766</td>\n",
       "      <td>-0.915791</td>\n",
       "      <td>-0.892201</td>\n",
       "      <td>-0.861610</td>\n",
       "      <td>-0.822807</td>\n",
       "      <td>-0.775161</td>\n",
       "      <td>-0.720509</td>\n",
       "      <td>-0.657577</td>\n",
       "      <td>-0.590236</td>\n",
       "      <td>-0.516277</td>\n",
       "      <td>-0.436586</td>\n",
       "      <td>-0.353475</td>\n",
       "      <td>-0.265478</td>\n",
       "      <td>-0.175722</td>\n",
       "      <td>-0.083128</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>0.107049</td>\n",
       "      <td>0.200372</td>\n",
       "      <td>0.291802</td>\n",
       "      <td>0.380818</td>\n",
       "      <td>0.464638</td>\n",
       "      <td>0.543544</td>\n",
       "      <td>0.618184</td>\n",
       "      <td>0.685508</td>\n",
       "      <td>0.746285</td>\n",
       "      <td>0.801600</td>\n",
       "      <td>0.848678</td>\n",
       "      <td>0.887792</td>\n",
       "      <td>0.919701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.952708</td>\n",
       "      <td>0.975942</td>\n",
       "      <td>0.991563</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991709</td>\n",
       "      <td>0.977037</td>\n",
       "      <td>0.952753</td>\n",
       "      <td>0.920753</td>\n",
       "      <td>0.879358</td>\n",
       "      <td>0.830320</td>\n",
       "      <td>0.771960</td>\n",
       "      <td>0.707504</td>\n",
       "      <td>0.637094</td>\n",
       "      <td>0.559174</td>\n",
       "      <td>0.476043</td>\n",
       "      <td>0.388265</td>\n",
       "      <td>0.297741</td>\n",
       "      <td>0.203110</td>\n",
       "      <td>0.105654</td>\n",
       "      <td>0.008265</td>\n",
       "      <td>-0.089971</td>\n",
       "      <td>-0.186878</td>\n",
       "      <td>-0.282889</td>\n",
       "      <td>-0.373850</td>\n",
       "      <td>-0.459772</td>\n",
       "      <td>-0.543938</td>\n",
       "      <td>-0.618809</td>\n",
       "      <td>-0.689893</td>\n",
       "      <td>-0.752930</td>\n",
       "      <td>-0.808538</td>\n",
       "      <td>-0.856858</td>\n",
       "      <td>-0.898133</td>\n",
       "      <td>-0.931444</td>\n",
       "      <td>-0.954546</td>\n",
       "      <td>-0.968768</td>\n",
       "      <td>-0.972605</td>\n",
       "      <td>-0.967541</td>\n",
       "      <td>-0.953349</td>\n",
       "      <td>-0.929832</td>\n",
       "      <td>-0.898754</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.449593</td>\n",
       "      <td>-0.527110</td>\n",
       "      <td>-0.601115</td>\n",
       "      <td>-0.667530</td>\n",
       "      <td>-0.726964</td>\n",
       "      <td>-0.780829</td>\n",
       "      <td>-0.827717</td>\n",
       "      <td>-0.866372</td>\n",
       "      <td>-0.896312</td>\n",
       "      <td>-0.918235</td>\n",
       "      <td>-0.931292</td>\n",
       "      <td>-0.935209</td>\n",
       "      <td>-0.930227</td>\n",
       "      <td>-0.915564</td>\n",
       "      <td>-0.893349</td>\n",
       "      <td>-0.862633</td>\n",
       "      <td>-0.822899</td>\n",
       "      <td>-0.775505</td>\n",
       "      <td>-0.719193</td>\n",
       "      <td>-0.657321</td>\n",
       "      <td>-0.589585</td>\n",
       "      <td>-0.513922</td>\n",
       "      <td>-0.435912</td>\n",
       "      <td>-0.351854</td>\n",
       "      <td>-0.264674</td>\n",
       "      <td>-0.174169</td>\n",
       "      <td>-0.080343</td>\n",
       "      <td>0.014956</td>\n",
       "      <td>0.109322</td>\n",
       "      <td>0.202778</td>\n",
       "      <td>0.294689</td>\n",
       "      <td>0.382559</td>\n",
       "      <td>0.465656</td>\n",
       "      <td>0.544603</td>\n",
       "      <td>0.618901</td>\n",
       "      <td>0.685304</td>\n",
       "      <td>0.747358</td>\n",
       "      <td>0.801540</td>\n",
       "      <td>0.848424</td>\n",
       "      <td>0.888553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.920379</td>\n",
       "      <td>0.951701</td>\n",
       "      <td>0.976653</td>\n",
       "      <td>0.991709</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991797</td>\n",
       "      <td>0.976600</td>\n",
       "      <td>0.953059</td>\n",
       "      <td>0.920219</td>\n",
       "      <td>0.878866</td>\n",
       "      <td>0.829082</td>\n",
       "      <td>0.770851</td>\n",
       "      <td>0.706819</td>\n",
       "      <td>0.634635</td>\n",
       "      <td>0.556631</td>\n",
       "      <td>0.473407</td>\n",
       "      <td>0.386596</td>\n",
       "      <td>0.294844</td>\n",
       "      <td>0.199396</td>\n",
       "      <td>0.102986</td>\n",
       "      <td>0.004642</td>\n",
       "      <td>-0.093316</td>\n",
       "      <td>-0.191504</td>\n",
       "      <td>-0.285541</td>\n",
       "      <td>-0.375372</td>\n",
       "      <td>-0.464131</td>\n",
       "      <td>-0.544198</td>\n",
       "      <td>-0.621180</td>\n",
       "      <td>-0.690446</td>\n",
       "      <td>-0.752927</td>\n",
       "      <td>-0.808632</td>\n",
       "      <td>-0.857843</td>\n",
       "      <td>-0.899581</td>\n",
       "      <td>-0.931478</td>\n",
       "      <td>-0.954942</td>\n",
       "      <td>-0.968104</td>\n",
       "      <td>-0.972371</td>\n",
       "      <td>-0.967530</td>\n",
       "      <td>-0.953390</td>\n",
       "      <td>-0.930406</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.369489</td>\n",
       "      <td>-0.451352</td>\n",
       "      <td>-0.530330</td>\n",
       "      <td>-0.602213</td>\n",
       "      <td>-0.667637</td>\n",
       "      <td>-0.728035</td>\n",
       "      <td>-0.782096</td>\n",
       "      <td>-0.828058</td>\n",
       "      <td>-0.866163</td>\n",
       "      <td>-0.896505</td>\n",
       "      <td>-0.918420</td>\n",
       "      <td>-0.931313</td>\n",
       "      <td>-0.935350</td>\n",
       "      <td>-0.929760</td>\n",
       "      <td>-0.916158</td>\n",
       "      <td>-0.893899</td>\n",
       "      <td>-0.862374</td>\n",
       "      <td>-0.822427</td>\n",
       "      <td>-0.773510</td>\n",
       "      <td>-0.718309</td>\n",
       "      <td>-0.656165</td>\n",
       "      <td>-0.587024</td>\n",
       "      <td>-0.512938</td>\n",
       "      <td>-0.433628</td>\n",
       "      <td>-0.350130</td>\n",
       "      <td>-0.262032</td>\n",
       "      <td>-0.170492</td>\n",
       "      <td>-0.076240</td>\n",
       "      <td>0.018144</td>\n",
       "      <td>0.112633</td>\n",
       "      <td>0.206547</td>\n",
       "      <td>0.297122</td>\n",
       "      <td>0.384374</td>\n",
       "      <td>0.467405</td>\n",
       "      <td>0.546141</td>\n",
       "      <td>0.619342</td>\n",
       "      <td>0.686979</td>\n",
       "      <td>0.747683</td>\n",
       "      <td>0.801726</td>\n",
       "      <td>0.849458</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",
       "      <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",
       "      <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",
       "      <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>123</th>\n",
       "      <td>0.847121</td>\n",
       "      <td>0.800081</td>\n",
       "      <td>0.746285</td>\n",
       "      <td>0.685304</td>\n",
       "      <td>0.619342</td>\n",
       "      <td>0.547000</td>\n",
       "      <td>0.470335</td>\n",
       "      <td>0.388718</td>\n",
       "      <td>0.302121</td>\n",
       "      <td>0.214125</td>\n",
       "      <td>0.121665</td>\n",
       "      <td>0.029448</td>\n",
       "      <td>-0.063301</td>\n",
       "      <td>-0.156220</td>\n",
       "      <td>-0.246673</td>\n",
       "      <td>-0.335415</td>\n",
       "      <td>-0.419091</td>\n",
       "      <td>-0.498484</td>\n",
       "      <td>-0.575232</td>\n",
       "      <td>-0.644806</td>\n",
       "      <td>-0.708504</td>\n",
       "      <td>-0.766043</td>\n",
       "      <td>-0.816103</td>\n",
       "      <td>-0.857074</td>\n",
       "      <td>-0.888801</td>\n",
       "      <td>-0.913941</td>\n",
       "      <td>-0.927646</td>\n",
       "      <td>-0.932674</td>\n",
       "      <td>-0.928923</td>\n",
       "      <td>-0.915369</td>\n",
       "      <td>-0.892829</td>\n",
       "      <td>-0.862265</td>\n",
       "      <td>-0.824002</td>\n",
       "      <td>-0.777919</td>\n",
       "      <td>-0.724100</td>\n",
       "      <td>-0.664305</td>\n",
       "      <td>-0.599586</td>\n",
       "      <td>-0.527920</td>\n",
       "      <td>-0.452469</td>\n",
       "      <td>-0.372245</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.932678</td>\n",
       "      <td>-0.955941</td>\n",
       "      <td>-0.970547</td>\n",
       "      <td>-0.974813</td>\n",
       "      <td>-0.969006</td>\n",
       "      <td>-0.954008</td>\n",
       "      <td>-0.929663</td>\n",
       "      <td>-0.897120</td>\n",
       "      <td>-0.855052</td>\n",
       "      <td>-0.806020</td>\n",
       "      <td>-0.749129</td>\n",
       "      <td>-0.685546</td>\n",
       "      <td>-0.616621</td>\n",
       "      <td>-0.540408</td>\n",
       "      <td>-0.461610</td>\n",
       "      <td>-0.376944</td>\n",
       "      <td>-0.286648</td>\n",
       "      <td>-0.195853</td>\n",
       "      <td>-0.098597</td>\n",
       "      <td>-0.002228</td>\n",
       "      <td>0.094792</td>\n",
       "      <td>0.191966</td>\n",
       "      <td>0.285633</td>\n",
       "      <td>0.377452</td>\n",
       "      <td>0.464632</td>\n",
       "      <td>0.548155</td>\n",
       "      <td>0.627414</td>\n",
       "      <td>0.701317</td>\n",
       "      <td>0.767334</td>\n",
       "      <td>0.826228</td>\n",
       "      <td>0.878270</td>\n",
       "      <td>0.920376</td>\n",
       "      <td>0.953481</td>\n",
       "      <td>0.978395</td>\n",
       "      <td>0.993024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992828</td>\n",
       "      <td>0.978218</td>\n",
       "      <td>0.953091</td>\n",
       "      <td>0.920956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.887535</td>\n",
       "      <td>0.848119</td>\n",
       "      <td>0.801600</td>\n",
       "      <td>0.747358</td>\n",
       "      <td>0.686979</td>\n",
       "      <td>0.621274</td>\n",
       "      <td>0.549169</td>\n",
       "      <td>0.471694</td>\n",
       "      <td>0.389534</td>\n",
       "      <td>0.304198</td>\n",
       "      <td>0.213853</td>\n",
       "      <td>0.122717</td>\n",
       "      <td>0.030169</td>\n",
       "      <td>-0.063637</td>\n",
       "      <td>-0.156236</td>\n",
       "      <td>-0.247338</td>\n",
       "      <td>-0.334739</td>\n",
       "      <td>-0.419222</td>\n",
       "      <td>-0.499773</td>\n",
       "      <td>-0.575545</td>\n",
       "      <td>-0.645088</td>\n",
       "      <td>-0.709457</td>\n",
       "      <td>-0.767218</td>\n",
       "      <td>-0.816129</td>\n",
       "      <td>-0.856193</td>\n",
       "      <td>-0.890293</td>\n",
       "      <td>-0.913026</td>\n",
       "      <td>-0.927613</td>\n",
       "      <td>-0.933059</td>\n",
       "      <td>-0.928576</td>\n",
       "      <td>-0.915163</td>\n",
       "      <td>-0.893324</td>\n",
       "      <td>-0.863407</td>\n",
       "      <td>-0.824885</td>\n",
       "      <td>-0.778312</td>\n",
       "      <td>-0.725028</td>\n",
       "      <td>-0.666256</td>\n",
       "      <td>-0.600163</td>\n",
       "      <td>-0.529647</td>\n",
       "      <td>-0.453843</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.900944</td>\n",
       "      <td>-0.933169</td>\n",
       "      <td>-0.957238</td>\n",
       "      <td>-0.971066</td>\n",
       "      <td>-0.974856</td>\n",
       "      <td>-0.969476</td>\n",
       "      <td>-0.954575</td>\n",
       "      <td>-0.930938</td>\n",
       "      <td>-0.897383</td>\n",
       "      <td>-0.856290</td>\n",
       "      <td>-0.807052</td>\n",
       "      <td>-0.750009</td>\n",
       "      <td>-0.687565</td>\n",
       "      <td>-0.617032</td>\n",
       "      <td>-0.543313</td>\n",
       "      <td>-0.463357</td>\n",
       "      <td>-0.376900</td>\n",
       "      <td>-0.289043</td>\n",
       "      <td>-0.194037</td>\n",
       "      <td>-0.098869</td>\n",
       "      <td>-0.002015</td>\n",
       "      <td>0.096078</td>\n",
       "      <td>0.191661</td>\n",
       "      <td>0.286350</td>\n",
       "      <td>0.377266</td>\n",
       "      <td>0.465290</td>\n",
       "      <td>0.549826</td>\n",
       "      <td>0.629647</td>\n",
       "      <td>0.702216</td>\n",
       "      <td>0.768252</td>\n",
       "      <td>0.828011</td>\n",
       "      <td>0.878368</td>\n",
       "      <td>0.920213</td>\n",
       "      <td>0.953691</td>\n",
       "      <td>0.978641</td>\n",
       "      <td>0.992828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993273</td>\n",
       "      <td>0.978240</td>\n",
       "      <td>0.954607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.919003</td>\n",
       "      <td>0.887529</td>\n",
       "      <td>0.848678</td>\n",
       "      <td>0.801540</td>\n",
       "      <td>0.747683</td>\n",
       "      <td>0.687628</td>\n",
       "      <td>0.621808</td>\n",
       "      <td>0.548976</td>\n",
       "      <td>0.470971</td>\n",
       "      <td>0.389969</td>\n",
       "      <td>0.302149</td>\n",
       "      <td>0.213005</td>\n",
       "      <td>0.121601</td>\n",
       "      <td>0.027853</td>\n",
       "      <td>-0.065680</td>\n",
       "      <td>-0.158787</td>\n",
       "      <td>-0.248488</td>\n",
       "      <td>-0.336845</td>\n",
       "      <td>-0.422310</td>\n",
       "      <td>-0.502363</td>\n",
       "      <td>-0.578005</td>\n",
       "      <td>-0.647932</td>\n",
       "      <td>-0.712200</td>\n",
       "      <td>-0.768495</td>\n",
       "      <td>-0.816274</td>\n",
       "      <td>-0.858694</td>\n",
       "      <td>-0.889953</td>\n",
       "      <td>-0.913594</td>\n",
       "      <td>-0.928168</td>\n",
       "      <td>-0.932567</td>\n",
       "      <td>-0.928222</td>\n",
       "      <td>-0.915363</td>\n",
       "      <td>-0.894030</td>\n",
       "      <td>-0.863530</td>\n",
       "      <td>-0.824378</td>\n",
       "      <td>-0.778084</td>\n",
       "      <td>-0.725631</td>\n",
       "      <td>-0.665521</td>\n",
       "      <td>-0.600403</td>\n",
       "      <td>-0.529497</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861549</td>\n",
       "      <td>-0.902115</td>\n",
       "      <td>-0.935101</td>\n",
       "      <td>-0.958071</td>\n",
       "      <td>-0.971212</td>\n",
       "      <td>-0.975365</td>\n",
       "      <td>-0.969952</td>\n",
       "      <td>-0.955390</td>\n",
       "      <td>-0.930651</td>\n",
       "      <td>-0.897851</td>\n",
       "      <td>-0.856433</td>\n",
       "      <td>-0.806823</td>\n",
       "      <td>-0.750648</td>\n",
       "      <td>-0.686634</td>\n",
       "      <td>-0.618410</td>\n",
       "      <td>-0.543485</td>\n",
       "      <td>-0.461825</td>\n",
       "      <td>-0.377374</td>\n",
       "      <td>-0.285628</td>\n",
       "      <td>-0.192633</td>\n",
       "      <td>-0.096944</td>\n",
       "      <td>0.001018</td>\n",
       "      <td>0.097518</td>\n",
       "      <td>0.194139</td>\n",
       "      <td>0.287885</td>\n",
       "      <td>0.379590</td>\n",
       "      <td>0.468616</td>\n",
       "      <td>0.553622</td>\n",
       "      <td>0.632002</td>\n",
       "      <td>0.704470</td>\n",
       "      <td>0.771214</td>\n",
       "      <td>0.829114</td>\n",
       "      <td>0.879007</td>\n",
       "      <td>0.920992</td>\n",
       "      <td>0.954453</td>\n",
       "      <td>0.978218</td>\n",
       "      <td>0.993273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992650</td>\n",
       "      <td>0.978871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0.941647</td>\n",
       "      <td>0.918357</td>\n",
       "      <td>0.887792</td>\n",
       "      <td>0.848424</td>\n",
       "      <td>0.801726</td>\n",
       "      <td>0.748357</td>\n",
       "      <td>0.688226</td>\n",
       "      <td>0.622028</td>\n",
       "      <td>0.548789</td>\n",
       "      <td>0.471823</td>\n",
       "      <td>0.388826</td>\n",
       "      <td>0.302155</td>\n",
       "      <td>0.212978</td>\n",
       "      <td>0.120344</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>-0.066993</td>\n",
       "      <td>-0.158691</td>\n",
       "      <td>-0.249434</td>\n",
       "      <td>-0.338609</td>\n",
       "      <td>-0.422982</td>\n",
       "      <td>-0.502702</td>\n",
       "      <td>-0.578948</td>\n",
       "      <td>-0.648761</td>\n",
       "      <td>-0.712036</td>\n",
       "      <td>-0.767251</td>\n",
       "      <td>-0.817599</td>\n",
       "      <td>-0.857004</td>\n",
       "      <td>-0.889471</td>\n",
       "      <td>-0.912922</td>\n",
       "      <td>-0.926699</td>\n",
       "      <td>-0.931165</td>\n",
       "      <td>-0.927588</td>\n",
       "      <td>-0.915409</td>\n",
       "      <td>-0.893510</td>\n",
       "      <td>-0.862696</td>\n",
       "      <td>-0.823651</td>\n",
       "      <td>-0.778371</td>\n",
       "      <td>-0.724919</td>\n",
       "      <td>-0.665887</td>\n",
       "      <td>-0.600605</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.812941</td>\n",
       "      <td>-0.861436</td>\n",
       "      <td>-0.902964</td>\n",
       "      <td>-0.934785</td>\n",
       "      <td>-0.957123</td>\n",
       "      <td>-0.970824</td>\n",
       "      <td>-0.975079</td>\n",
       "      <td>-0.969916</td>\n",
       "      <td>-0.954444</td>\n",
       "      <td>-0.930512</td>\n",
       "      <td>-0.897546</td>\n",
       "      <td>-0.855891</td>\n",
       "      <td>-0.807262</td>\n",
       "      <td>-0.749635</td>\n",
       "      <td>-0.687982</td>\n",
       "      <td>-0.618891</td>\n",
       "      <td>-0.542347</td>\n",
       "      <td>-0.462794</td>\n",
       "      <td>-0.374521</td>\n",
       "      <td>-0.285013</td>\n",
       "      <td>-0.191587</td>\n",
       "      <td>-0.094888</td>\n",
       "      <td>0.001411</td>\n",
       "      <td>0.098898</td>\n",
       "      <td>0.194522</td>\n",
       "      <td>0.289003</td>\n",
       "      <td>0.381721</td>\n",
       "      <td>0.471182</td>\n",
       "      <td>0.554758</td>\n",
       "      <td>0.633034</td>\n",
       "      <td>0.706203</td>\n",
       "      <td>0.771113</td>\n",
       "      <td>0.828537</td>\n",
       "      <td>0.878568</td>\n",
       "      <td>0.920633</td>\n",
       "      <td>0.953091</td>\n",
       "      <td>0.978240</td>\n",
       "      <td>0.992650</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0.955655</td>\n",
       "      <td>0.941725</td>\n",
       "      <td>0.919701</td>\n",
       "      <td>0.888553</td>\n",
       "      <td>0.849458</td>\n",
       "      <td>0.803143</td>\n",
       "      <td>0.749498</td>\n",
       "      <td>0.689087</td>\n",
       "      <td>0.622173</td>\n",
       "      <td>0.549802</td>\n",
       "      <td>0.470721</td>\n",
       "      <td>0.388269</td>\n",
       "      <td>0.301934</td>\n",
       "      <td>0.211312</td>\n",
       "      <td>0.118826</td>\n",
       "      <td>0.024947</td>\n",
       "      <td>-0.067726</td>\n",
       "      <td>-0.160610</td>\n",
       "      <td>-0.252017</td>\n",
       "      <td>-0.340384</td>\n",
       "      <td>-0.425297</td>\n",
       "      <td>-0.505661</td>\n",
       "      <td>-0.582096</td>\n",
       "      <td>-0.650582</td>\n",
       "      <td>-0.712313</td>\n",
       "      <td>-0.770389</td>\n",
       "      <td>-0.817555</td>\n",
       "      <td>-0.858420</td>\n",
       "      <td>-0.890419</td>\n",
       "      <td>-0.913126</td>\n",
       "      <td>-0.927008</td>\n",
       "      <td>-0.932442</td>\n",
       "      <td>-0.929365</td>\n",
       "      <td>-0.916318</td>\n",
       "      <td>-0.894106</td>\n",
       "      <td>-0.863219</td>\n",
       "      <td>-0.825061</td>\n",
       "      <td>-0.778755</td>\n",
       "      <td>-0.726169</td>\n",
       "      <td>-0.666897</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.758676</td>\n",
       "      <td>-0.814383</td>\n",
       "      <td>-0.863990</td>\n",
       "      <td>-0.904236</td>\n",
       "      <td>-0.935404</td>\n",
       "      <td>-0.958380</td>\n",
       "      <td>-0.972190</td>\n",
       "      <td>-0.976445</td>\n",
       "      <td>-0.970404</td>\n",
       "      <td>-0.955593</td>\n",
       "      <td>-0.931434</td>\n",
       "      <td>-0.898164</td>\n",
       "      <td>-0.857286</td>\n",
       "      <td>-0.807278</td>\n",
       "      <td>-0.751947</td>\n",
       "      <td>-0.689267</td>\n",
       "      <td>-0.618661</td>\n",
       "      <td>-0.543779</td>\n",
       "      <td>-0.460556</td>\n",
       "      <td>-0.374356</td>\n",
       "      <td>-0.284013</td>\n",
       "      <td>-0.189364</td>\n",
       "      <td>-0.094102</td>\n",
       "      <td>0.003361</td>\n",
       "      <td>0.100024</td>\n",
       "      <td>0.196519</td>\n",
       "      <td>0.292191</td>\n",
       "      <td>0.385464</td>\n",
       "      <td>0.473644</td>\n",
       "      <td>0.557228</td>\n",
       "      <td>0.636282</td>\n",
       "      <td>0.707708</td>\n",
       "      <td>0.772163</td>\n",
       "      <td>0.829755</td>\n",
       "      <td>0.879899</td>\n",
       "      <td>0.920956</td>\n",
       "      <td>0.954607</td>\n",
       "      <td>0.978871</td>\n",
       "      <td>0.993065</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2    ...       125       126       127\n",
       "0    1.000000  0.990863  0.976753  ...  0.919003  0.941647  0.955655\n",
       "1    0.990863  1.000000  0.990991  ...  0.887529  0.918357  0.941725\n",
       "2    0.976753  0.990991  1.000000  ...  0.848678  0.887792  0.919701\n",
       "3    0.952708  0.975942  0.991563  ...  0.801540  0.848424  0.888553\n",
       "4    0.920379  0.951701  0.976653  ...  0.747683  0.801726  0.849458\n",
       "..        ...       ...       ...  ...       ...       ...       ...\n",
       "123  0.847121  0.800081  0.746285  ...  0.978218  0.953091  0.920956\n",
       "124  0.887535  0.848119  0.801600  ...  0.993273  0.978240  0.954607\n",
       "125  0.919003  0.887529  0.848678  ...  1.000000  0.992650  0.978871\n",
       "126  0.941647  0.918357  0.887792  ...  0.992650  1.000000  0.993065\n",
       "127  0.955655  0.941725  0.919701  ...  0.978871  0.993065  1.000000\n",
       "\n",
       "[128 rows x 128 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "7h91sk4xUy3p"
   },
   "outputs": [],
   "source": [
    "data2=pd.read_csv('Voltage_L1_DataSet2.csv',names=col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m5YD-hg6ZWgd",
    "outputId": "8ffeb1a4-0cb8-418d-b809-2acfd53fedb3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 128)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "vNszhQpgZZGl",
    "outputId": "4c9e2281-4163-4701-8ca9-39d12aca4194"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>...</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4079.423420</td>\n",
       "      <td>-3707.734514</td>\n",
       "      <td>-3300.341749</td>\n",
       "      <td>-2860.906131</td>\n",
       "      <td>-2394.006186</td>\n",
       "      <td>-1905.134733</td>\n",
       "      <td>-1396.121912</td>\n",
       "      <td>-875.208453</td>\n",
       "      <td>-345.139021</td>\n",
       "      <td>188.591309</td>\n",
       "      <td>719.575600</td>\n",
       "      <td>1244.151093</td>\n",
       "      <td>1755.910641</td>\n",
       "      <td>2251.190671</td>\n",
       "      <td>2724.499037</td>\n",
       "      <td>3172.174014</td>\n",
       "      <td>3588.721326</td>\n",
       "      <td>4120.621043</td>\n",
       "      <td>4445.620212</td>\n",
       "      <td>4729.422432</td>\n",
       "      <td>4969.280884</td>\n",
       "      <td>5156.956131</td>\n",
       "      <td>5297.941481</td>\n",
       "      <td>5386.744103</td>\n",
       "      <td>5423.363946</td>\n",
       "      <td>5407.799984</td>\n",
       "      <td>5340.054575</td>\n",
       "      <td>5221.040774</td>\n",
       "      <td>5051.674494</td>\n",
       "      <td>4831.957140</td>\n",
       "      <td>4569.211541</td>\n",
       "      <td>4260.691688</td>\n",
       "      <td>3909.142885</td>\n",
       "      <td>3522.806754</td>\n",
       "      <td>3100.765313</td>\n",
       "      <td>2649.428425</td>\n",
       "      <td>2172.458402</td>\n",
       "      <td>1674.432229</td>\n",
       "      <td>1159.926472</td>\n",
       "      <td>634.434638</td>\n",
       "      <td>...</td>\n",
       "      <td>5423.364296</td>\n",
       "      <td>5407.800281</td>\n",
       "      <td>5340.969836</td>\n",
       "      <td>5221.955933</td>\n",
       "      <td>5051.675201</td>\n",
       "      <td>4832.872592</td>\n",
       "      <td>4569.211714</td>\n",
       "      <td>4261.606710</td>\n",
       "      <td>3910.059130</td>\n",
       "      <td>3523.721765</td>\n",
       "      <td>3101.680453</td>\n",
       "      <td>2650.343902</td>\n",
       "      <td>2173.373895</td>\n",
       "      <td>1675.347587</td>\n",
       "      <td>1160.841629</td>\n",
       "      <td>635.350225</td>\n",
       "      <td>104.366810</td>\n",
       "      <td>-428.448514</td>\n",
       "      <td>-957.602170</td>\n",
       "      <td>-1477.600846</td>\n",
       "      <td>-1982.951212</td>\n",
       "      <td>-2469.076387</td>\n",
       "      <td>-2932.314559</td>\n",
       "      <td>-3366.256296</td>\n",
       "      <td>-3769.072038</td>\n",
       "      <td>-4135.267874</td>\n",
       "      <td>-4460.266860</td>\n",
       "      <td>-4744.069262</td>\n",
       "      <td>-4983.927939</td>\n",
       "      <td>-5172.518148</td>\n",
       "      <td>-5312.588575</td>\n",
       "      <td>-5402.306276</td>\n",
       "      <td>-5438.926563</td>\n",
       "      <td>-5424.278739</td>\n",
       "      <td>-5357.447199</td>\n",
       "      <td>-5238.433473</td>\n",
       "      <td>-5069.068331</td>\n",
       "      <td>-4849.350422</td>\n",
       "      <td>-4587.520555</td>\n",
       "      <td>-4279.915768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4918.927835</td>\n",
       "      <td>-4668.999112</td>\n",
       "      <td>-4375.127119</td>\n",
       "      <td>-4034.564431</td>\n",
       "      <td>-3659.213495</td>\n",
       "      <td>-3247.243211</td>\n",
       "      <td>-2804.145676</td>\n",
       "      <td>-2333.584172</td>\n",
       "      <td>-1841.965323</td>\n",
       "      <td>-1332.037736</td>\n",
       "      <td>-808.377367</td>\n",
       "      <td>-278.308933</td>\n",
       "      <td>254.506887</td>\n",
       "      <td>784.575542</td>\n",
       "      <td>1308.235862</td>\n",
       "      <td>1818.164165</td>\n",
       "      <td>2310.697156</td>\n",
       "      <td>2781.259036</td>\n",
       "      <td>3225.271917</td>\n",
       "      <td>3637.242750</td>\n",
       "      <td>4014.424716</td>\n",
       "      <td>4352.240339</td>\n",
       "      <td>4648.859005</td>\n",
       "      <td>4902.449844</td>\n",
       "      <td>5105.688883</td>\n",
       "      <td>5260.406079</td>\n",
       "      <td>5365.688297</td>\n",
       "      <td>5417.871144</td>\n",
       "      <td>5417.870532</td>\n",
       "      <td>5365.687865</td>\n",
       "      <td>5262.237541</td>\n",
       "      <td>5107.520025</td>\n",
       "      <td>4902.449637</td>\n",
       "      <td>4652.521530</td>\n",
       "      <td>4358.648701</td>\n",
       "      <td>4019.001605</td>\n",
       "      <td>3642.735492</td>\n",
       "      <td>3230.765388</td>\n",
       "      <td>2787.667859</td>\n",
       "      <td>2318.020920</td>\n",
       "      <td>...</td>\n",
       "      <td>5105.689233</td>\n",
       "      <td>5260.406376</td>\n",
       "      <td>5365.688069</td>\n",
       "      <td>5417.870813</td>\n",
       "      <td>5417.871239</td>\n",
       "      <td>5366.603317</td>\n",
       "      <td>5262.237714</td>\n",
       "      <td>5107.519556</td>\n",
       "      <td>4903.365881</td>\n",
       "      <td>4652.521051</td>\n",
       "      <td>4358.648351</td>\n",
       "      <td>4019.001592</td>\n",
       "      <td>3642.735495</td>\n",
       "      <td>3230.765256</td>\n",
       "      <td>2788.583015</td>\n",
       "      <td>2318.021017</td>\n",
       "      <td>1826.403675</td>\n",
       "      <td>1315.560114</td>\n",
       "      <td>793.730379</td>\n",
       "      <td>262.745822</td>\n",
       "      <td>-270.069247</td>\n",
       "      <td>-801.053437</td>\n",
       "      <td>-1323.798465</td>\n",
       "      <td>-1833.725880</td>\n",
       "      <td>-2326.259651</td>\n",
       "      <td>-2796.821357</td>\n",
       "      <td>-3239.918565</td>\n",
       "      <td>-3652.805070</td>\n",
       "      <td>-4029.071772</td>\n",
       "      <td>-4367.802356</td>\n",
       "      <td>-4663.506099</td>\n",
       "      <td>-4918.012016</td>\n",
       "      <td>-5121.251501</td>\n",
       "      <td>-5275.969344</td>\n",
       "      <td>-5381.249941</td>\n",
       "      <td>-5433.432863</td>\n",
       "      <td>-5434.348878</td>\n",
       "      <td>-5382.165656</td>\n",
       "      <td>-5278.715575</td>\n",
       "      <td>-5123.997634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2920.413836</td>\n",
       "      <td>2376.612648</td>\n",
       "      <td>1773.304349</td>\n",
       "      <td>1123.306756</td>\n",
       "      <td>444.013104</td>\n",
       "      <td>-248.097664</td>\n",
       "      <td>-933.799415</td>\n",
       "      <td>-1594.783666</td>\n",
       "      <td>-2213.654301</td>\n",
       "      <td>-2776.681104</td>\n",
       "      <td>-3272.876698</td>\n",
       "      <td>-3696.748942</td>\n",
       "      <td>-4044.634592</td>\n",
       "      <td>-4320.197220</td>\n",
       "      <td>-4527.097994</td>\n",
       "      <td>-4673.576088</td>\n",
       "      <td>-4806.322831</td>\n",
       "      <td>-4853.012854</td>\n",
       "      <td>-4879.561881</td>\n",
       "      <td>-4892.378451</td>\n",
       "      <td>-4896.040365</td>\n",
       "      <td>-4895.125096</td>\n",
       "      <td>-4886.885810</td>\n",
       "      <td>-4868.575925</td>\n",
       "      <td>-4831.040592</td>\n",
       "      <td>-4765.125936</td>\n",
       "      <td>-4661.674697</td>\n",
       "      <td>-4508.787940</td>\n",
       "      <td>-4291.817400</td>\n",
       "      <td>-4009.846183</td>\n",
       "      <td>-3651.889500</td>\n",
       "      <td>-3220.693357</td>\n",
       "      <td>-2715.343432</td>\n",
       "      <td>-2144.992415</td>\n",
       "      <td>-1519.713189</td>\n",
       "      <td>-855.067653</td>\n",
       "      <td>-167.534277</td>\n",
       "      <td>523.661181</td>\n",
       "      <td>1201.123527</td>\n",
       "      <td>1846.543522</td>\n",
       "      <td>...</td>\n",
       "      <td>-4831.040242</td>\n",
       "      <td>-4764.210149</td>\n",
       "      <td>-4661.674926</td>\n",
       "      <td>-4507.872781</td>\n",
       "      <td>-4290.901203</td>\n",
       "      <td>-4008.930731</td>\n",
       "      <td>-3650.973837</td>\n",
       "      <td>-3217.947354</td>\n",
       "      <td>-2712.596207</td>\n",
       "      <td>-2142.246424</td>\n",
       "      <td>-1516.967069</td>\n",
       "      <td>-852.321196</td>\n",
       "      <td>-164.787804</td>\n",
       "      <td>527.323010</td>\n",
       "      <td>1203.869664</td>\n",
       "      <td>1848.374599</td>\n",
       "      <td>2445.274979</td>\n",
       "      <td>2981.752084</td>\n",
       "      <td>3447.736160</td>\n",
       "      <td>3838.650127</td>\n",
       "      <td>4155.409865</td>\n",
       "      <td>4400.761275</td>\n",
       "      <td>4580.197149</td>\n",
       "      <td>4706.535349</td>\n",
       "      <td>4790.760336</td>\n",
       "      <td>4837.450533</td>\n",
       "      <td>4863.999743</td>\n",
       "      <td>4875.900641</td>\n",
       "      <td>4880.477819</td>\n",
       "      <td>4879.563079</td>\n",
       "      <td>4871.323226</td>\n",
       "      <td>4852.098262</td>\n",
       "      <td>4814.562485</td>\n",
       "      <td>4748.647181</td>\n",
       "      <td>4645.197563</td>\n",
       "      <td>4492.310731</td>\n",
       "      <td>4274.423564</td>\n",
       "      <td>3991.537412</td>\n",
       "      <td>3633.580485</td>\n",
       "      <td>3201.469277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2808.723170</td>\n",
       "      <td>-2303.372710</td>\n",
       "      <td>-1758.656432</td>\n",
       "      <td>-1180.066319</td>\n",
       "      <td>-579.504820</td>\n",
       "      <td>32.957795</td>\n",
       "      <td>643.590017</td>\n",
       "      <td>1241.404644</td>\n",
       "      <td>1815.417600</td>\n",
       "      <td>2356.471851</td>\n",
       "      <td>2854.498498</td>\n",
       "      <td>3304.003804</td>\n",
       "      <td>3702.242579</td>\n",
       "      <td>4046.466745</td>\n",
       "      <td>4336.677092</td>\n",
       "      <td>4574.704837</td>\n",
       "      <td>4766.957077</td>\n",
       "      <td>4916.181934</td>\n",
       "      <td>5023.294461</td>\n",
       "      <td>5098.364940</td>\n",
       "      <td>5139.562041</td>\n",
       "      <td>5152.378681</td>\n",
       "      <td>5135.899735</td>\n",
       "      <td>5090.125313</td>\n",
       "      <td>5011.393404</td>\n",
       "      <td>4897.872002</td>\n",
       "      <td>4743.155034</td>\n",
       "      <td>4548.155555</td>\n",
       "      <td>4301.888108</td>\n",
       "      <td>4003.438606</td>\n",
       "      <td>3652.805957</td>\n",
       "      <td>3247.244154</td>\n",
       "      <td>2791.329481</td>\n",
       "      <td>2285.979638</td>\n",
       "      <td>1741.262524</td>\n",
       "      <td>1162.672513</td>\n",
       "      <td>562.111328</td>\n",
       "      <td>-49.435617</td>\n",
       "      <td>-660.067834</td>\n",
       "      <td>-1257.883386</td>\n",
       "      <td>...</td>\n",
       "      <td>5012.309244</td>\n",
       "      <td>4897.872299</td>\n",
       "      <td>4744.070295</td>\n",
       "      <td>4549.070714</td>\n",
       "      <td>4302.804305</td>\n",
       "      <td>4004.354058</td>\n",
       "      <td>3653.721620</td>\n",
       "      <td>3249.074666</td>\n",
       "      <td>2792.245726</td>\n",
       "      <td>2288.725629</td>\n",
       "      <td>1743.093154</td>\n",
       "      <td>1164.503480</td>\n",
       "      <td>564.857801</td>\n",
       "      <td>-47.604769</td>\n",
       "      <td>-657.321697</td>\n",
       "      <td>-1256.052309</td>\n",
       "      <td>-1830.063758</td>\n",
       "      <td>-2370.203003</td>\n",
       "      <td>-2868.229995</td>\n",
       "      <td>-3318.651424</td>\n",
       "      <td>-3715.973959</td>\n",
       "      <td>-4060.198170</td>\n",
       "      <td>-4350.408714</td>\n",
       "      <td>-4589.351061</td>\n",
       "      <td>-4781.604082</td>\n",
       "      <td>-4931.744255</td>\n",
       "      <td>-5038.856599</td>\n",
       "      <td>-5113.011770</td>\n",
       "      <td>-5155.124587</td>\n",
       "      <td>-5167.940697</td>\n",
       "      <td>-5151.462319</td>\n",
       "      <td>-5106.602976</td>\n",
       "      <td>-5027.871511</td>\n",
       "      <td>-4914.350757</td>\n",
       "      <td>-4759.632168</td>\n",
       "      <td>-4565.548254</td>\n",
       "      <td>-4319.281944</td>\n",
       "      <td>-4020.831887</td>\n",
       "      <td>-3671.114971</td>\n",
       "      <td>-3265.552744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2871.892861</td>\n",
       "      <td>2532.245964</td>\n",
       "      <td>2055.275298</td>\n",
       "      <td>1435.488878</td>\n",
       "      <td>704.927781</td>\n",
       "      <td>-86.971408</td>\n",
       "      <td>-871.546088</td>\n",
       "      <td>-1584.713275</td>\n",
       "      <td>-2177.034698</td>\n",
       "      <td>-2625.625239</td>\n",
       "      <td>-2937.807324</td>\n",
       "      <td>-3148.370376</td>\n",
       "      <td>-3312.242517</td>\n",
       "      <td>-3484.354765</td>\n",
       "      <td>-3713.227301</td>\n",
       "      <td>-4027.240082</td>\n",
       "      <td>-4419.986012</td>\n",
       "      <td>-4863.083246</td>\n",
       "      <td>-5298.856344</td>\n",
       "      <td>-5666.883070</td>\n",
       "      <td>-5907.656918</td>\n",
       "      <td>-5982.727327</td>\n",
       "      <td>-5873.784131</td>\n",
       "      <td>-5606.460940</td>\n",
       "      <td>-5218.292902</td>\n",
       "      <td>-4776.111817</td>\n",
       "      <td>-4338.506694</td>\n",
       "      <td>-3959.493884</td>\n",
       "      <td>-3662.875706</td>\n",
       "      <td>-3447.735266</td>\n",
       "      <td>-3281.116012</td>\n",
       "      <td>-3113.581016</td>\n",
       "      <td>-2888.371059</td>\n",
       "      <td>-2548.723546</td>\n",
       "      <td>-2073.584696</td>\n",
       "      <td>-1453.798174</td>\n",
       "      <td>-722.321274</td>\n",
       "      <td>69.578095</td>\n",
       "      <td>853.237291</td>\n",
       "      <td>1566.403553</td>\n",
       "      <td>...</td>\n",
       "      <td>-5219.208042</td>\n",
       "      <td>-4777.027010</td>\n",
       "      <td>-4340.337903</td>\n",
       "      <td>-3960.409705</td>\n",
       "      <td>-3663.790489</td>\n",
       "      <td>-3447.735304</td>\n",
       "      <td>-3281.115839</td>\n",
       "      <td>-3114.496974</td>\n",
       "      <td>-2889.285795</td>\n",
       "      <td>-2549.639515</td>\n",
       "      <td>-2074.500536</td>\n",
       "      <td>-1454.713678</td>\n",
       "      <td>-724.152251</td>\n",
       "      <td>66.831493</td>\n",
       "      <td>851.405978</td>\n",
       "      <td>1565.488160</td>\n",
       "      <td>2158.726579</td>\n",
       "      <td>2608.232126</td>\n",
       "      <td>2920.413866</td>\n",
       "      <td>3131.891775</td>\n",
       "      <td>3295.764667</td>\n",
       "      <td>3467.876869</td>\n",
       "      <td>3696.749208</td>\n",
       "      <td>4010.762877</td>\n",
       "      <td>4403.508027</td>\n",
       "      <td>4846.605434</td>\n",
       "      <td>5283.294206</td>\n",
       "      <td>5651.320750</td>\n",
       "      <td>5892.094372</td>\n",
       "      <td>5967.165311</td>\n",
       "      <td>5858.221546</td>\n",
       "      <td>5590.898767</td>\n",
       "      <td>5202.730284</td>\n",
       "      <td>4759.633062</td>\n",
       "      <td>4322.945050</td>\n",
       "      <td>3943.932165</td>\n",
       "      <td>3647.312850</td>\n",
       "      <td>3432.172965</td>\n",
       "      <td>3265.553468</td>\n",
       "      <td>3098.018896</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",
       "      <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",
       "      <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",
       "      <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>5995</th>\n",
       "      <td>-2559.506480</td>\n",
       "      <td>-2032.758277</td>\n",
       "      <td>-1451.098940</td>\n",
       "      <td>-858.252650</td>\n",
       "      <td>-299.981913</td>\n",
       "      <td>68.131815</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>40.675881</td>\n",
       "      <td>52.878717</td>\n",
       "      <td>71.182601</td>\n",
       "      <td>85.419418</td>\n",
       "      <td>101.689242</td>\n",
       "      <td>113.891987</td>\n",
       "      <td>128.128299</td>\n",
       "      <td>140.330768</td>\n",
       "      <td>151.516898</td>\n",
       "      <td>159.652312</td>\n",
       "      <td>167.786625</td>\n",
       "      <td>169.820574</td>\n",
       "      <td>173.888033</td>\n",
       "      <td>174.905457</td>\n",
       "      <td>175.922091</td>\n",
       "      <td>174.905601</td>\n",
       "      <td>172.871785</td>\n",
       "      <td>166.770497</td>\n",
       "      <td>159.652316</td>\n",
       "      <td>151.516937</td>\n",
       "      <td>140.331329</td>\n",
       "      <td>130.162235</td>\n",
       "      <td>115.925759</td>\n",
       "      <td>102.706120</td>\n",
       "      <td>88.469636</td>\n",
       "      <td>71.182290</td>\n",
       "      <td>54.912893</td>\n",
       "      <td>36.608417</td>\n",
       "      <td>18.304260</td>\n",
       "      <td>1.017739</td>\n",
       "      <td>-17.286218</td>\n",
       "      <td>-34.574078</td>\n",
       "      <td>-50.844027</td>\n",
       "      <td>...</td>\n",
       "      <td>5681.353564</td>\n",
       "      <td>5432.215859</td>\n",
       "      <td>5168.841824</td>\n",
       "      <td>4815.982269</td>\n",
       "      <td>4460.071091</td>\n",
       "      <td>4033.994767</td>\n",
       "      <td>3583.514041</td>\n",
       "      <td>3100.491838</td>\n",
       "      <td>2535.102299</td>\n",
       "      <td>1993.101215</td>\n",
       "      <td>1422.626846</td>\n",
       "      <td>833.848711</td>\n",
       "      <td>303.033497</td>\n",
       "      <td>-312.183782</td>\n",
       "      <td>-877.573914</td>\n",
       "      <td>-1433.811087</td>\n",
       "      <td>-2012.421054</td>\n",
       "      <td>-2545.270065</td>\n",
       "      <td>-3092.355858</td>\n",
       "      <td>-3626.222357</td>\n",
       "      <td>-4031.960366</td>\n",
       "      <td>-4459.053140</td>\n",
       "      <td>-4886.146606</td>\n",
       "      <td>-5145.452828</td>\n",
       "      <td>-5410.860792</td>\n",
       "      <td>-5682.369515</td>\n",
       "      <td>-5768.804448</td>\n",
       "      <td>-5878.628758</td>\n",
       "      <td>-5957.946040</td>\n",
       "      <td>-5971.165895</td>\n",
       "      <td>-6001.672502</td>\n",
       "      <td>-5959.979895</td>\n",
       "      <td>-5883.713610</td>\n",
       "      <td>-5696.605756</td>\n",
       "      <td>-5462.722047</td>\n",
       "      <td>-5202.398569</td>\n",
       "      <td>-4867.842027</td>\n",
       "      <td>-4573.961696</td>\n",
       "      <td>-4172.291112</td>\n",
       "      <td>-3737.062497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>-5903.034108</td>\n",
       "      <td>-5742.365573</td>\n",
       "      <td>-5486.110384</td>\n",
       "      <td>-5207.482475</td>\n",
       "      <td>-4847.504891</td>\n",
       "      <td>-4488.543155</td>\n",
       "      <td>-4046.196413</td>\n",
       "      <td>-3614.019464</td>\n",
       "      <td>-3107.609077</td>\n",
       "      <td>-2559.506564</td>\n",
       "      <td>-2032.758212</td>\n",
       "      <td>-1451.098680</td>\n",
       "      <td>-858.252907</td>\n",
       "      <td>-299.981534</td>\n",
       "      <td>68.131722</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>40.676420</td>\n",
       "      <td>52.878285</td>\n",
       "      <td>71.182442</td>\n",
       "      <td>85.418780</td>\n",
       "      <td>101.689523</td>\n",
       "      <td>113.891925</td>\n",
       "      <td>128.128755</td>\n",
       "      <td>140.331370</td>\n",
       "      <td>151.517177</td>\n",
       "      <td>159.652316</td>\n",
       "      <td>167.787144</td>\n",
       "      <td>169.821080</td>\n",
       "      <td>173.888417</td>\n",
       "      <td>174.905260</td>\n",
       "      <td>175.922054</td>\n",
       "      <td>174.905113</td>\n",
       "      <td>172.871086</td>\n",
       "      <td>166.770569</td>\n",
       "      <td>159.651861</td>\n",
       "      <td>151.516583</td>\n",
       "      <td>140.331390</td>\n",
       "      <td>130.162536</td>\n",
       "      <td>115.925340</td>\n",
       "      <td>102.706056</td>\n",
       "      <td>...</td>\n",
       "      <td>5106.811864</td>\n",
       "      <td>5458.654946</td>\n",
       "      <td>5680.336470</td>\n",
       "      <td>5814.566251</td>\n",
       "      <td>5875.579138</td>\n",
       "      <td>5924.389493</td>\n",
       "      <td>5938.626568</td>\n",
       "      <td>5945.744363</td>\n",
       "      <td>5856.258392</td>\n",
       "      <td>5681.353863</td>\n",
       "      <td>5432.216091</td>\n",
       "      <td>5168.842105</td>\n",
       "      <td>4815.982284</td>\n",
       "      <td>4460.071437</td>\n",
       "      <td>4033.994955</td>\n",
       "      <td>3583.514131</td>\n",
       "      <td>3100.491633</td>\n",
       "      <td>2535.102207</td>\n",
       "      <td>1993.100853</td>\n",
       "      <td>1422.626388</td>\n",
       "      <td>833.848546</td>\n",
       "      <td>303.033199</td>\n",
       "      <td>-312.184541</td>\n",
       "      <td>-877.574040</td>\n",
       "      <td>-1433.811962</td>\n",
       "      <td>-2012.420850</td>\n",
       "      <td>-2545.269600</td>\n",
       "      <td>-3092.355735</td>\n",
       "      <td>-3626.221937</td>\n",
       "      <td>-4031.960546</td>\n",
       "      <td>-4459.053460</td>\n",
       "      <td>-4886.146205</td>\n",
       "      <td>-5145.452948</td>\n",
       "      <td>-5410.860237</td>\n",
       "      <td>-5682.369847</td>\n",
       "      <td>-5768.805166</td>\n",
       "      <td>-5878.628664</td>\n",
       "      <td>-5957.946216</td>\n",
       "      <td>-5971.165921</td>\n",
       "      <td>-6001.671994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>-61.177211</td>\n",
       "      <td>-670.042497</td>\n",
       "      <td>-1249.082477</td>\n",
       "      <td>-1837.467121</td>\n",
       "      <td>-2388.422613</td>\n",
       "      <td>-2928.476587</td>\n",
       "      <td>-3455.128977</td>\n",
       "      <td>-3916.358235</td>\n",
       "      <td>-4348.661905</td>\n",
       "      <td>-4762.331598</td>\n",
       "      <td>-5091.611546</td>\n",
       "      <td>-5413.110653</td>\n",
       "      <td>-5643.275069</td>\n",
       "      <td>-5855.748697</td>\n",
       "      <td>-5974.738674</td>\n",
       "      <td>-6060.992204</td>\n",
       "      <td>-6079.751864</td>\n",
       "      <td>-6038.897122</td>\n",
       "      <td>-5965.081552</td>\n",
       "      <td>-5809.411569</td>\n",
       "      <td>-5575.835392</td>\n",
       "      <td>-5306.781885</td>\n",
       "      <td>-5002.565077</td>\n",
       "      <td>-4654.828237</td>\n",
       "      <td>-4222.612256</td>\n",
       "      <td>-3785.844197</td>\n",
       "      <td>-3291.536050</td>\n",
       "      <td>-2794.878778</td>\n",
       "      <td>-2237.520943</td>\n",
       "      <td>-1665.424749</td>\n",
       "      <td>-1092.756036</td>\n",
       "      <td>-504.386204</td>\n",
       "      <td>116.330230</td>\n",
       "      <td>692.365492</td>\n",
       "      <td>1280.238486</td>\n",
       "      <td>1877.570176</td>\n",
       "      <td>2434.617405</td>\n",
       "      <td>2982.463454</td>\n",
       "      <td>3483.975428</td>\n",
       "      <td>3945.605773</td>\n",
       "      <td>...</td>\n",
       "      <td>-4193.537591</td>\n",
       "      <td>-3771.100455</td>\n",
       "      <td>-3316.776161</td>\n",
       "      <td>-2782.848067</td>\n",
       "      <td>-2252.518873</td>\n",
       "      <td>-1676.152597</td>\n",
       "      <td>-1097.085713</td>\n",
       "      <td>-494.964445</td>\n",
       "      <td>87.465707</td>\n",
       "      <td>684.342567</td>\n",
       "      <td>1302.479212</td>\n",
       "      <td>1880.372549</td>\n",
       "      <td>2416.401988</td>\n",
       "      <td>2957.560849</td>\n",
       "      <td>3464.594579</td>\n",
       "      <td>3936.880279</td>\n",
       "      <td>4408.007865</td>\n",
       "      <td>4803.384396</td>\n",
       "      <td>5122.165468</td>\n",
       "      <td>5453.079196</td>\n",
       "      <td>5703.155978</td>\n",
       "      <td>5864.043552</td>\n",
       "      <td>6027.462939</td>\n",
       "      <td>6082.768506</td>\n",
       "      <td>6107.730311</td>\n",
       "      <td>6089.789527</td>\n",
       "      <td>5997.526193</td>\n",
       "      <td>5830.164154</td>\n",
       "      <td>5636.812001</td>\n",
       "      <td>5372.013022</td>\n",
       "      <td>5045.573142</td>\n",
       "      <td>4676.460927</td>\n",
       "      <td>4255.209133</td>\n",
       "      <td>3825.542606</td>\n",
       "      <td>3319.478597</td>\n",
       "      <td>2812.997831</td>\n",
       "      <td>2281.814658</td>\n",
       "      <td>1696.762241</td>\n",
       "      <td>1126.421734</td>\n",
       "      <td>516.713927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>474.479154</td>\n",
       "      <td>-128.850780</td>\n",
       "      <td>-729.827759</td>\n",
       "      <td>-1316.385901</td>\n",
       "      <td>-1895.899437</td>\n",
       "      <td>-2450.305884</td>\n",
       "      <td>-2989.118773</td>\n",
       "      <td>-3496.978647</td>\n",
       "      <td>-3964.037680</td>\n",
       "      <td>-4404.918464</td>\n",
       "      <td>-4793.704989</td>\n",
       "      <td>-5136.979717</td>\n",
       "      <td>-5442.204793</td>\n",
       "      <td>-5685.872901</td>\n",
       "      <td>-5866.945102</td>\n",
       "      <td>-6002.146084</td>\n",
       "      <td>-1799.093370</td>\n",
       "      <td>-5242.358253</td>\n",
       "      <td>-5623.008918</td>\n",
       "      <td>-5951.939474</td>\n",
       "      <td>-5795.604412</td>\n",
       "      <td>-5578.579224</td>\n",
       "      <td>-5308.825660</td>\n",
       "      <td>-4994.999170</td>\n",
       "      <td>-4628.681219</td>\n",
       "      <td>-4210.593255</td>\n",
       "      <td>-3760.675046</td>\n",
       "      <td>-3270.554887</td>\n",
       "      <td>-2750.713203</td>\n",
       "      <td>-2202.912061</td>\n",
       "      <td>-1636.653943</td>\n",
       "      <td>-1055.339927</td>\n",
       "      <td>-460.776409</td>\n",
       "      <td>138.313813</td>\n",
       "      <td>739.001059</td>\n",
       "      <td>1323.993926</td>\n",
       "      <td>1899.497123</td>\n",
       "      <td>2465.122490</td>\n",
       "      <td>3001.629605</td>\n",
       "      <td>3508.352627</td>\n",
       "      <td>...</td>\n",
       "      <td>-4627.150411</td>\n",
       "      <td>-4215.317246</td>\n",
       "      <td>-3757.862628</td>\n",
       "      <td>-3277.895106</td>\n",
       "      <td>-2750.542415</td>\n",
       "      <td>-2205.309839</td>\n",
       "      <td>-1634.979331</td>\n",
       "      <td>-1055.478968</td>\n",
       "      <td>-464.819506</td>\n",
       "      <td>142.144782</td>\n",
       "      <td>1701.682466</td>\n",
       "      <td>1515.514681</td>\n",
       "      <td>1996.498567</td>\n",
       "      <td>2458.895426</td>\n",
       "      <td>2993.528624</td>\n",
       "      <td>3502.552765</td>\n",
       "      <td>3973.617080</td>\n",
       "      <td>4415.346422</td>\n",
       "      <td>4802.266583</td>\n",
       "      <td>5154.365687</td>\n",
       "      <td>5449.879785</td>\n",
       "      <td>5686.549477</td>\n",
       "      <td>5882.763742</td>\n",
       "      <td>6009.121840</td>\n",
       "      <td>6091.475302</td>\n",
       "      <td>6103.928884</td>\n",
       "      <td>6062.868233</td>\n",
       "      <td>5957.459486</td>\n",
       "      <td>5802.658718</td>\n",
       "      <td>5589.346743</td>\n",
       "      <td>5320.112627</td>\n",
       "      <td>5002.133416</td>\n",
       "      <td>4634.596230</td>\n",
       "      <td>4222.163396</td>\n",
       "      <td>3770.392539</td>\n",
       "      <td>3286.430795</td>\n",
       "      <td>2761.417139</td>\n",
       "      <td>2219.856080</td>\n",
       "      <td>1647.914351</td>\n",
       "      <td>1068.422217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>4130.300397</td>\n",
       "      <td>3667.818740</td>\n",
       "      <td>3161.901475</td>\n",
       "      <td>2639.861069</td>\n",
       "      <td>2077.117887</td>\n",
       "      <td>1511.900686</td>\n",
       "      <td>938.148661</td>\n",
       "      <td>332.649119</td>\n",
       "      <td>-265.726759</td>\n",
       "      <td>-856.874362</td>\n",
       "      <td>-1458.744830</td>\n",
       "      <td>-2029.492097</td>\n",
       "      <td>-2588.742562</td>\n",
       "      <td>-3106.241784</td>\n",
       "      <td>-3598.026168</td>\n",
       "      <td>-4074.145199</td>\n",
       "      <td>-4501.313989</td>\n",
       "      <td>-4885.049760</td>\n",
       "      <td>-5215.873857</td>\n",
       "      <td>-5491.092378</td>\n",
       "      <td>-3535.758629</td>\n",
       "      <td>-5463.659128</td>\n",
       "      <td>-5797.904412</td>\n",
       "      <td>-6081.178222</td>\n",
       "      <td>-6091.560102</td>\n",
       "      <td>-6024.244519</td>\n",
       "      <td>-5914.682383</td>\n",
       "      <td>-5746.413952</td>\n",
       "      <td>-5508.314876</td>\n",
       "      <td>-5238.632391</td>\n",
       "      <td>-4905.690031</td>\n",
       "      <td>-4517.556120</td>\n",
       "      <td>-4109.123413</td>\n",
       "      <td>-3646.658064</td>\n",
       "      <td>-3152.708716</td>\n",
       "      <td>-2618.841180</td>\n",
       "      <td>-2062.177407</td>\n",
       "      <td>-1494.766157</td>\n",
       "      <td>-896.118970</td>\n",
       "      <td>-318.729090</td>\n",
       "      <td>...</td>\n",
       "      <td>-6091.186401</td>\n",
       "      <td>-3388.923387</td>\n",
       "      <td>-5385.192712</td>\n",
       "      <td>-1385.117843</td>\n",
       "      <td>-4650.207026</td>\n",
       "      <td>-4793.054425</td>\n",
       "      <td>-4901.668705</td>\n",
       "      <td>-4531.049921</td>\n",
       "      <td>-4093.670476</td>\n",
       "      <td>-3636.542421</td>\n",
       "      <td>-3134.025267</td>\n",
       "      <td>-2612.746923</td>\n",
       "      <td>-2074.678887</td>\n",
       "      <td>-1489.148139</td>\n",
       "      <td>-896.724052</td>\n",
       "      <td>-318.801012</td>\n",
       "      <td>294.234995</td>\n",
       "      <td>883.888904</td>\n",
       "      <td>1477.622633</td>\n",
       "      <td>2034.941299</td>\n",
       "      <td>2604.947533</td>\n",
       "      <td>3121.476329</td>\n",
       "      <td>3620.926962</td>\n",
       "      <td>5213.041017</td>\n",
       "      <td>4736.051573</td>\n",
       "      <td>5006.030209</td>\n",
       "      <td>5239.787268</td>\n",
       "      <td>5509.474207</td>\n",
       "      <td>5755.371121</td>\n",
       "      <td>5916.284265</td>\n",
       "      <td>6045.121993</td>\n",
       "      <td>6106.337399</td>\n",
       "      <td>6099.695587</td>\n",
       "      <td>6038.702985</td>\n",
       "      <td>5941.911123</td>\n",
       "      <td>5764.681679</td>\n",
       "      <td>5527.425018</td>\n",
       "      <td>5251.753143</td>\n",
       "      <td>4931.818120</td>\n",
       "      <td>4536.825758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0            1    ...          126          127\n",
       "0    -4079.423420 -3707.734514  ... -4587.520555 -4279.915768\n",
       "1    -4918.927835 -4668.999112  ... -5278.715575 -5123.997634\n",
       "2     2920.413836  2376.612648  ...  3633.580485  3201.469277\n",
       "3    -2808.723170 -2303.372710  ... -3671.114971 -3265.552744\n",
       "4     2871.892861  2532.245964  ...  3265.553468  3098.018896\n",
       "...           ...          ...  ...          ...          ...\n",
       "5995 -2559.506480 -2032.758277  ... -4172.291112 -3737.062497\n",
       "5996 -5903.034108 -5742.365573  ... -5971.165921 -6001.671994\n",
       "5997   -61.177211  -670.042497  ...  1126.421734   516.713927\n",
       "5998   474.479154  -128.850780  ...  1647.914351  1068.422217\n",
       "5999  4130.300397  3667.818740  ...  4931.818120  4536.825758\n",
       "\n",
       "[6000 rows x 128 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining the Input and Output Datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "MxbDVIWQZbSG"
   },
   "outputs": [],
   "source": [
    "data=pd.concat([data1,data2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e86zY1CnZyCh",
    "outputId": "a73f27d4-ac3f-45ea-e749-9c0efd0f7e24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 128)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "id": "vegPP_eOZzWl",
    "outputId": "00e56828-6aab-444f-e086-826fa148078a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2464.190673</td>\n",
       "      <td>-2925.256244</td>\n",
       "      <td>-3358.931979</td>\n",
       "      <td>-3760.652243</td>\n",
       "      <td>-4124.939861</td>\n",
       "      <td>-4449.055880</td>\n",
       "      <td>-4732.086714</td>\n",
       "      <td>-4971.292771</td>\n",
       "      <td>-5158.457976</td>\n",
       "      <td>-5299.060129</td>\n",
       "      <td>...</td>\n",
       "      <td>2644.052851</td>\n",
       "      <td>2168.379577</td>\n",
       "      <td>1671.705859</td>\n",
       "      <td>1157.686541</td>\n",
       "      <td>634.536384</td>\n",
       "      <td>104.083017</td>\n",
       "      <td>-427.284371</td>\n",
       "      <td>-954.999435</td>\n",
       "      <td>-1472.671036</td>\n",
       "      <td>-1976.648032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-797.962914</td>\n",
       "      <td>-1320.199586</td>\n",
       "      <td>-1828.741445</td>\n",
       "      <td>-2319.935857</td>\n",
       "      <td>-2789.218649</td>\n",
       "      <td>-3231.111864</td>\n",
       "      <td>-3641.962908</td>\n",
       "      <td>-4018.119193</td>\n",
       "      <td>-4355.929647</td>\n",
       "      <td>-4650.829055</td>\n",
       "      <td>...</td>\n",
       "      <td>4008.077109</td>\n",
       "      <td>3632.834002</td>\n",
       "      <td>3221.982426</td>\n",
       "      <td>2780.090228</td>\n",
       "      <td>2311.720161</td>\n",
       "      <td>1821.438861</td>\n",
       "      <td>1311.983509</td>\n",
       "      <td>790.659456</td>\n",
       "      <td>262.031836</td>\n",
       "      <td>-269.335205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4387.885674</td>\n",
       "      <td>4567.747164</td>\n",
       "      <td>4693.741356</td>\n",
       "      <td>4776.824895</td>\n",
       "      <td>4824.300958</td>\n",
       "      <td>4850.777519</td>\n",
       "      <td>4862.646175</td>\n",
       "      <td>4867.211521</td>\n",
       "      <td>4866.298624</td>\n",
       "      <td>4858.081691</td>\n",
       "      <td>...</td>\n",
       "      <td>-850.916937</td>\n",
       "      <td>-165.252287</td>\n",
       "      <td>524.062959</td>\n",
       "      <td>1199.684611</td>\n",
       "      <td>1842.437384</td>\n",
       "      <td>2437.714882</td>\n",
       "      <td>2972.733259</td>\n",
       "      <td>3437.450839</td>\n",
       "      <td>3828.215741</td>\n",
       "      <td>4143.201102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4050.074299</td>\n",
       "      <td>-4340.408587</td>\n",
       "      <td>-4576.875995</td>\n",
       "      <td>-4769.518913</td>\n",
       "      <td>-4918.338175</td>\n",
       "      <td>-5025.159834</td>\n",
       "      <td>-5100.026323</td>\n",
       "      <td>-5141.111052</td>\n",
       "      <td>-5153.892968</td>\n",
       "      <td>-5137.458861</td>\n",
       "      <td>...</td>\n",
       "      <td>1161.338395</td>\n",
       "      <td>563.322919</td>\n",
       "      <td>-47.475988</td>\n",
       "      <td>-656.447463</td>\n",
       "      <td>-1252.637741</td>\n",
       "      <td>-1825.089177</td>\n",
       "      <td>-2364.673580</td>\n",
       "      <td>-2861.346591</td>\n",
       "      <td>-3309.630078</td>\n",
       "      <td>-3706.785897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3458.450134</td>\n",
       "      <td>3686.700705</td>\n",
       "      <td>4000.773209</td>\n",
       "      <td>4392.451258</td>\n",
       "      <td>4834.343975</td>\n",
       "      <td>5269.845213</td>\n",
       "      <td>5636.871457</td>\n",
       "      <td>5876.078192</td>\n",
       "      <td>5950.944421</td>\n",
       "      <td>5841.384319</td>\n",
       "      <td>...</td>\n",
       "      <td>-1450.758930</td>\n",
       "      <td>-722.183210</td>\n",
       "      <td>67.562203</td>\n",
       "      <td>850.005032</td>\n",
       "      <td>1561.232918</td>\n",
       "      <td>2152.858410</td>\n",
       "      <td>2601.141643</td>\n",
       "      <td>2912.474970</td>\n",
       "      <td>3123.378574</td>\n",
       "      <td>3286.805684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0            1            2            3            4    \\\n",
       "0 -2464.190673 -2925.256244 -3358.931979 -3760.652243 -4124.939861   \n",
       "1  -797.962914 -1320.199586 -1828.741445 -2319.935857 -2789.218649   \n",
       "2  4387.885674  4567.747164  4693.741356  4776.824895  4824.300958   \n",
       "3 -4050.074299 -4340.408587 -4576.875995 -4769.518913 -4918.338175   \n",
       "4  3458.450134  3686.700705  4000.773209  4392.451258  4834.343975   \n",
       "\n",
       "           5            6            7            8            9    ...  \\\n",
       "0 -4449.055880 -4732.086714 -4971.292771 -5158.457976 -5299.060129  ...   \n",
       "1 -3231.111864 -3641.962908 -4018.119193 -4355.929647 -4650.829055  ...   \n",
       "2  4850.777519  4862.646175  4867.211521  4866.298624  4858.081691  ...   \n",
       "3 -5025.159834 -5100.026323 -5141.111052 -5153.892968 -5137.458861  ...   \n",
       "4  5269.845213  5636.871457  5876.078192  5950.944421  5841.384319  ...   \n",
       "\n",
       "           118          119          120          121          122  \\\n",
       "0  2644.052851  2168.379577  1671.705859  1157.686541   634.536384   \n",
       "1  4008.077109  3632.834002  3221.982426  2780.090228  2311.720161   \n",
       "2  -850.916937  -165.252287   524.062959  1199.684611  1842.437384   \n",
       "3  1161.338395   563.322919   -47.475988  -656.447463 -1252.637741   \n",
       "4 -1450.758930  -722.183210    67.562203   850.005032  1561.232918   \n",
       "\n",
       "           123          124          125          126          127  \n",
       "0   104.083017  -427.284371  -954.999435 -1472.671036 -1976.648032  \n",
       "1  1821.438861  1311.983509   790.659456   262.031836  -269.335205  \n",
       "2  2437.714882  2972.733259  3437.450839  3828.215741  4143.201102  \n",
       "3 -1825.089177 -2364.673580 -2861.346591 -3309.630078 -3706.785897  \n",
       "4  2152.858410  2601.141643  2912.474970  3123.378574  3286.805684  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "_ipIQEuKZ1Ni",
    "outputId": "6996845a-764c-4a0e-f3b2-684171b78d6f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2464.190673</td>\n",
       "      <td>-2925.256244</td>\n",
       "      <td>-3358.931979</td>\n",
       "      <td>-3760.652243</td>\n",
       "      <td>-4124.939861</td>\n",
       "      <td>-4449.055880</td>\n",
       "      <td>-4732.086714</td>\n",
       "      <td>-4971.292771</td>\n",
       "      <td>-5158.457976</td>\n",
       "      <td>-5299.060129</td>\n",
       "      <td>...</td>\n",
       "      <td>2644.052851</td>\n",
       "      <td>2168.379577</td>\n",
       "      <td>1671.705859</td>\n",
       "      <td>1157.686541</td>\n",
       "      <td>634.536384</td>\n",
       "      <td>104.083017</td>\n",
       "      <td>-427.284371</td>\n",
       "      <td>-954.999435</td>\n",
       "      <td>-1472.671036</td>\n",
       "      <td>-1976.648032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-797.962914</td>\n",
       "      <td>-1320.199586</td>\n",
       "      <td>-1828.741445</td>\n",
       "      <td>-2319.935857</td>\n",
       "      <td>-2789.218649</td>\n",
       "      <td>-3231.111864</td>\n",
       "      <td>-3641.962908</td>\n",
       "      <td>-4018.119193</td>\n",
       "      <td>-4355.929647</td>\n",
       "      <td>-4650.829055</td>\n",
       "      <td>...</td>\n",
       "      <td>4008.077109</td>\n",
       "      <td>3632.834002</td>\n",
       "      <td>3221.982426</td>\n",
       "      <td>2780.090228</td>\n",
       "      <td>2311.720161</td>\n",
       "      <td>1821.438861</td>\n",
       "      <td>1311.983509</td>\n",
       "      <td>790.659456</td>\n",
       "      <td>262.031836</td>\n",
       "      <td>-269.335205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4387.885674</td>\n",
       "      <td>4567.747164</td>\n",
       "      <td>4693.741356</td>\n",
       "      <td>4776.824895</td>\n",
       "      <td>4824.300958</td>\n",
       "      <td>4850.777519</td>\n",
       "      <td>4862.646175</td>\n",
       "      <td>4867.211521</td>\n",
       "      <td>4866.298624</td>\n",
       "      <td>4858.081691</td>\n",
       "      <td>...</td>\n",
       "      <td>-850.916937</td>\n",
       "      <td>-165.252287</td>\n",
       "      <td>524.062959</td>\n",
       "      <td>1199.684611</td>\n",
       "      <td>1842.437384</td>\n",
       "      <td>2437.714882</td>\n",
       "      <td>2972.733259</td>\n",
       "      <td>3437.450839</td>\n",
       "      <td>3828.215741</td>\n",
       "      <td>4143.201102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4050.074299</td>\n",
       "      <td>-4340.408587</td>\n",
       "      <td>-4576.875995</td>\n",
       "      <td>-4769.518913</td>\n",
       "      <td>-4918.338175</td>\n",
       "      <td>-5025.159834</td>\n",
       "      <td>-5100.026323</td>\n",
       "      <td>-5141.111052</td>\n",
       "      <td>-5153.892968</td>\n",
       "      <td>-5137.458861</td>\n",
       "      <td>...</td>\n",
       "      <td>1161.338395</td>\n",
       "      <td>563.322919</td>\n",
       "      <td>-47.475988</td>\n",
       "      <td>-656.447463</td>\n",
       "      <td>-1252.637741</td>\n",
       "      <td>-1825.089177</td>\n",
       "      <td>-2364.673580</td>\n",
       "      <td>-2861.346591</td>\n",
       "      <td>-3309.630078</td>\n",
       "      <td>-3706.785897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3458.450134</td>\n",
       "      <td>3686.700705</td>\n",
       "      <td>4000.773209</td>\n",
       "      <td>4392.451258</td>\n",
       "      <td>4834.343975</td>\n",
       "      <td>5269.845213</td>\n",
       "      <td>5636.871457</td>\n",
       "      <td>5876.078192</td>\n",
       "      <td>5950.944421</td>\n",
       "      <td>5841.384319</td>\n",
       "      <td>...</td>\n",
       "      <td>-1450.758930</td>\n",
       "      <td>-722.183210</td>\n",
       "      <td>67.562203</td>\n",
       "      <td>850.005032</td>\n",
       "      <td>1561.232918</td>\n",
       "      <td>2152.858410</td>\n",
       "      <td>2601.141643</td>\n",
       "      <td>2912.474970</td>\n",
       "      <td>3123.378574</td>\n",
       "      <td>3286.805684</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>11995</th>\n",
       "      <td>-2559.506480</td>\n",
       "      <td>-2032.758277</td>\n",
       "      <td>-1451.098940</td>\n",
       "      <td>-858.252650</td>\n",
       "      <td>-299.981913</td>\n",
       "      <td>68.131815</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>40.675881</td>\n",
       "      <td>52.878717</td>\n",
       "      <td>71.182601</td>\n",
       "      <td>...</td>\n",
       "      <td>-6001.672502</td>\n",
       "      <td>-5959.979895</td>\n",
       "      <td>-5883.713610</td>\n",
       "      <td>-5696.605756</td>\n",
       "      <td>-5462.722047</td>\n",
       "      <td>-5202.398569</td>\n",
       "      <td>-4867.842027</td>\n",
       "      <td>-4573.961696</td>\n",
       "      <td>-4172.291112</td>\n",
       "      <td>-3737.062497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>-5903.034108</td>\n",
       "      <td>-5742.365573</td>\n",
       "      <td>-5486.110384</td>\n",
       "      <td>-5207.482475</td>\n",
       "      <td>-4847.504891</td>\n",
       "      <td>-4488.543155</td>\n",
       "      <td>-4046.196413</td>\n",
       "      <td>-3614.019464</td>\n",
       "      <td>-3107.609077</td>\n",
       "      <td>-2559.506564</td>\n",
       "      <td>...</td>\n",
       "      <td>-4459.053460</td>\n",
       "      <td>-4886.146205</td>\n",
       "      <td>-5145.452948</td>\n",
       "      <td>-5410.860237</td>\n",
       "      <td>-5682.369847</td>\n",
       "      <td>-5768.805166</td>\n",
       "      <td>-5878.628664</td>\n",
       "      <td>-5957.946216</td>\n",
       "      <td>-5971.165921</td>\n",
       "      <td>-6001.671994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>-61.177211</td>\n",
       "      <td>-670.042497</td>\n",
       "      <td>-1249.082477</td>\n",
       "      <td>-1837.467121</td>\n",
       "      <td>-2388.422613</td>\n",
       "      <td>-2928.476587</td>\n",
       "      <td>-3455.128977</td>\n",
       "      <td>-3916.358235</td>\n",
       "      <td>-4348.661905</td>\n",
       "      <td>-4762.331598</td>\n",
       "      <td>...</td>\n",
       "      <td>5045.573142</td>\n",
       "      <td>4676.460927</td>\n",
       "      <td>4255.209133</td>\n",
       "      <td>3825.542606</td>\n",
       "      <td>3319.478597</td>\n",
       "      <td>2812.997831</td>\n",
       "      <td>2281.814658</td>\n",
       "      <td>1696.762241</td>\n",
       "      <td>1126.421734</td>\n",
       "      <td>516.713927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>474.479154</td>\n",
       "      <td>-128.850780</td>\n",
       "      <td>-729.827759</td>\n",
       "      <td>-1316.385901</td>\n",
       "      <td>-1895.899437</td>\n",
       "      <td>-2450.305884</td>\n",
       "      <td>-2989.118773</td>\n",
       "      <td>-3496.978647</td>\n",
       "      <td>-3964.037680</td>\n",
       "      <td>-4404.918464</td>\n",
       "      <td>...</td>\n",
       "      <td>5320.112627</td>\n",
       "      <td>5002.133416</td>\n",
       "      <td>4634.596230</td>\n",
       "      <td>4222.163396</td>\n",
       "      <td>3770.392539</td>\n",
       "      <td>3286.430795</td>\n",
       "      <td>2761.417139</td>\n",
       "      <td>2219.856080</td>\n",
       "      <td>1647.914351</td>\n",
       "      <td>1068.422217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>4130.300397</td>\n",
       "      <td>3667.818740</td>\n",
       "      <td>3161.901475</td>\n",
       "      <td>2639.861069</td>\n",
       "      <td>2077.117887</td>\n",
       "      <td>1511.900686</td>\n",
       "      <td>938.148661</td>\n",
       "      <td>332.649119</td>\n",
       "      <td>-265.726759</td>\n",
       "      <td>-856.874362</td>\n",
       "      <td>...</td>\n",
       "      <td>6045.121993</td>\n",
       "      <td>6106.337399</td>\n",
       "      <td>6099.695587</td>\n",
       "      <td>6038.702985</td>\n",
       "      <td>5941.911123</td>\n",
       "      <td>5764.681679</td>\n",
       "      <td>5527.425018</td>\n",
       "      <td>5251.753143</td>\n",
       "      <td>4931.818120</td>\n",
       "      <td>4536.825758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "0     -2464.190673 -2925.256244 -3358.931979 -3760.652243 -4124.939861   \n",
       "1      -797.962914 -1320.199586 -1828.741445 -2319.935857 -2789.218649   \n",
       "2      4387.885674  4567.747164  4693.741356  4776.824895  4824.300958   \n",
       "3     -4050.074299 -4340.408587 -4576.875995 -4769.518913 -4918.338175   \n",
       "4      3458.450134  3686.700705  4000.773209  4392.451258  4834.343975   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "11995 -2559.506480 -2032.758277 -1451.098940  -858.252650  -299.981913   \n",
       "11996 -5903.034108 -5742.365573 -5486.110384 -5207.482475 -4847.504891   \n",
       "11997   -61.177211  -670.042497 -1249.082477 -1837.467121 -2388.422613   \n",
       "11998   474.479154  -128.850780  -729.827759 -1316.385901 -1895.899437   \n",
       "11999  4130.300397  3667.818740  3161.901475  2639.861069  2077.117887   \n",
       "\n",
       "               5            6            7            8            9    ...  \\\n",
       "0     -4449.055880 -4732.086714 -4971.292771 -5158.457976 -5299.060129  ...   \n",
       "1     -3231.111864 -3641.962908 -4018.119193 -4355.929647 -4650.829055  ...   \n",
       "2      4850.777519  4862.646175  4867.211521  4866.298624  4858.081691  ...   \n",
       "3     -5025.159834 -5100.026323 -5141.111052 -5153.892968 -5137.458861  ...   \n",
       "4      5269.845213  5636.871457  5876.078192  5950.944421  5841.384319  ...   \n",
       "...            ...          ...          ...          ...          ...  ...   \n",
       "11995    68.131815     0.000799    40.675881    52.878717    71.182601  ...   \n",
       "11996 -4488.543155 -4046.196413 -3614.019464 -3107.609077 -2559.506564  ...   \n",
       "11997 -2928.476587 -3455.128977 -3916.358235 -4348.661905 -4762.331598  ...   \n",
       "11998 -2450.305884 -2989.118773 -3496.978647 -3964.037680 -4404.918464  ...   \n",
       "11999  1511.900686   938.148661   332.649119  -265.726759  -856.874362  ...   \n",
       "\n",
       "               118          119          120          121          122  \\\n",
       "0      2644.052851  2168.379577  1671.705859  1157.686541   634.536384   \n",
       "1      4008.077109  3632.834002  3221.982426  2780.090228  2311.720161   \n",
       "2      -850.916937  -165.252287   524.062959  1199.684611  1842.437384   \n",
       "3      1161.338395   563.322919   -47.475988  -656.447463 -1252.637741   \n",
       "4     -1450.758930  -722.183210    67.562203   850.005032  1561.232918   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "11995 -6001.672502 -5959.979895 -5883.713610 -5696.605756 -5462.722047   \n",
       "11996 -4459.053460 -4886.146205 -5145.452948 -5410.860237 -5682.369847   \n",
       "11997  5045.573142  4676.460927  4255.209133  3825.542606  3319.478597   \n",
       "11998  5320.112627  5002.133416  4634.596230  4222.163396  3770.392539   \n",
       "11999  6045.121993  6106.337399  6099.695587  6038.702985  5941.911123   \n",
       "\n",
       "               123          124          125          126          127  \n",
       "0       104.083017  -427.284371  -954.999435 -1472.671036 -1976.648032  \n",
       "1      1821.438861  1311.983509   790.659456   262.031836  -269.335205  \n",
       "2      2437.714882  2972.733259  3437.450839  3828.215741  4143.201102  \n",
       "3     -1825.089177 -2364.673580 -2861.346591 -3309.630078 -3706.785897  \n",
       "4      2152.858410  2601.141643  2912.474970  3123.378574  3286.805684  \n",
       "...            ...          ...          ...          ...          ...  \n",
       "11995 -5202.398569 -4867.842027 -4573.961696 -4172.291112 -3737.062497  \n",
       "11996 -5768.805166 -5878.628664 -5957.946216 -5971.165921 -6001.671994  \n",
       "11997  2812.997831  2281.814658  1696.762241  1126.421734   516.713927  \n",
       "11998  3286.430795  2761.417139  2219.856080  1647.914351  1068.422217  \n",
       "11999  5764.681679  5527.425018  5251.753143  4931.818120  4536.825758  \n",
       "\n",
       "[12000 rows x 128 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reset_index(inplace=True,drop=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "NpXDLjpjZ5Rz"
   },
   "outputs": [],
   "source": [
    "out_data1=pd.read_csv('OutputFor_DataSet1.csv',names=['output'])\n",
    "out_data2=pd.read_csv('OutputFor_DataSet2.csv',names=['output'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "vo2E0U9Hb672"
   },
   "outputs": [],
   "source": [
    "out_data=pd.concat([out_data1,out_data2])\n",
    "out_data.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ztpjYdSUb9Gl",
    "outputId": "79e6c115-5854-46dc-b35e-3c63f79fc210"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Train and Test Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "_QrsEhENb-uN"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(data,out_data,test_size=0.33, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ay1cSqN_rtVk",
    "outputId": "124126ae-1540-4a91-d88b-592341b44280"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8040, 128) (8040, 1)\n",
      "(3960, 128) (3960, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sfslRpfgrwwo",
    "outputId": "1b4766dc-0379-4b48-ddbb-14c13d69ff27"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling and Transforming the datasets using PCA (Principal Component Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hZFu3nhxsP_p"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)\n",
    "train_sc= scaler.transform(x_train)\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qQY-Q26t6LS",
    "outputId": "2e8327f5-d1d0-4799-a85f-156174d8de37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=50, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(50)\n",
    "pca.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BJIShu4EuNP6",
    "outputId": "65d2cdd0-0886-463f-c687-cd9d50fd9ae0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oPpAEwMuuiNN"
   },
   "outputs": [],
   "source": [
    "test_sc=scaler.transform(x_test)\n",
    "train_sc= scaler.transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XR0nX6MDusba"
   },
   "outputs": [],
   "source": [
    "train_sc = pca.transform(train_sc)\n",
    "test_sc = pca.transform(test_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nz_pw6zpu05-",
    "outputId": "a5b0d577-4d5d-4631-8cb7-5d3f11502efa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 84,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr = LogisticRegression(solver = 'lbfgs',max_iter=2000)\n",
    "logreg.fit(train_sc, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3JAHsHu7vHyv"
   },
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(test_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c4VIJZMTvUdT",
    "outputId": "9e1d35ab-2b59-4d2d-e876-89ad5f428cc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.26\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(test_sc, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HgYNJwjZvbvk",
    "outputId": "f6533986-3291-4b91-8033-042207dbb9b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr1 = LogisticRegression(solver = 'lbfgs',max_iter=1000)\n",
    "logisticRegr1.fit(scaler.transform(x_train), y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B2wmH0tAvyv3",
    "outputId": "39e39005-1fad-441b-cf54-87990bb767d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.64\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(scaler.transform(x_test), y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a Keras Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "GfBXo9iBwZIW"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout,Activation\n",
    "model = Sequential()\n",
    "model.add(Dense(64,activation='relu', input_dim=128))\n",
    "model.add(Dense(32,activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(16,activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(6, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SVd15jKr3cl3",
    "outputId": "1eda001c-3829-46ac-d7b3-542fe7f761f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 6)                 102       \n",
      "=================================================================\n",
      "Total params: 10,966\n",
      "Trainable params: 10,966\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3vq9Ksd73fEy"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ml4nKlcZ4Iox",
    "outputId": "05719a3c-73e2-4a07-a39b-b87a2feb962f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "252/252 [==============================] - 1s 1ms/step - loss: 1.6391 - accuracy: 0.3126\n",
      "Epoch 2/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.2256 - accuracy: 0.4009\n",
      "Epoch 3/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1948 - accuracy: 0.4047\n",
      "Epoch 4/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1561 - accuracy: 0.4196\n",
      "Epoch 5/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1434 - accuracy: 0.4331\n",
      "Epoch 6/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1386 - accuracy: 0.4438\n",
      "Epoch 7/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1141 - accuracy: 0.4701\n",
      "Epoch 8/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.0336 - accuracy: 0.5360\n",
      "Epoch 9/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.8342 - accuracy: 0.6414\n",
      "Epoch 10/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.7458 - accuracy: 0.6623\n",
      "Epoch 11/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6341 - accuracy: 0.7170\n",
      "Epoch 12/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5700 - accuracy: 0.7396\n",
      "Epoch 13/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5032 - accuracy: 0.7688\n",
      "Epoch 14/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4121 - accuracy: 0.7918\n",
      "Epoch 15/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4141 - accuracy: 0.7961\n",
      "Epoch 16/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3834 - accuracy: 0.8097\n",
      "Epoch 17/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3682 - accuracy: 0.8086\n",
      "Epoch 18/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3958 - accuracy: 0.8012\n",
      "Epoch 19/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3465 - accuracy: 0.8110\n",
      "Epoch 20/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3529 - accuracy: 0.8122\n",
      "Epoch 21/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3251 - accuracy: 0.8222\n",
      "Epoch 22/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3718 - accuracy: 0.8111\n",
      "Epoch 23/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5025 - accuracy: 0.7819\n",
      "Epoch 24/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3326 - accuracy: 0.8181\n",
      "Epoch 25/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3786 - accuracy: 0.8083\n",
      "Epoch 26/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3123 - accuracy: 0.8250\n",
      "Epoch 27/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3658 - accuracy: 0.8057\n",
      "Epoch 28/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3299 - accuracy: 0.8172\n",
      "Epoch 29/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8181\n",
      "Epoch 30/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3147 - accuracy: 0.8298\n",
      "Epoch 31/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3059 - accuracy: 0.8268\n",
      "Epoch 32/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3033 - accuracy: 0.8218\n",
      "Epoch 33/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4733 - accuracy: 0.7870\n",
      "Epoch 34/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4397 - accuracy: 0.7929\n",
      "Epoch 35/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3778 - accuracy: 0.7958\n",
      "Epoch 36/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2972 - accuracy: 0.8322\n",
      "Epoch 37/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3563 - accuracy: 0.8130\n",
      "Epoch 38/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2942 - accuracy: 0.8348\n",
      "Epoch 39/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3043 - accuracy: 0.8281\n",
      "Epoch 40/40\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3113 - accuracy: 0.8179\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fab196527d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "y_train1=np_utils.to_categorical(np.array(y_train),6)\n",
    "model.fit(scaler.transform(x_train),y_train1,batch_size=32,epochs=40,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KPnT4V4b4QP6",
    "outputId": "3d909163-61e4-49e3-a67f-eaf5dd3cec71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124/124 [==============================] - 0s 811us/step - loss: 0.3403 - accuracy: 0.8250\n",
      "\n",
      "accuracy: 82.50%\n"
     ]
    }
   ],
   "source": [
    "metrics=model.evaluate(scaler.transform(x_test),np_utils.to_categorical(np.array(y_test),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (model.metrics_names[1], metrics[1]*100))\n",
    "predictions = model.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sR_mFVQe5ms7"
   },
   "outputs": [],
   "source": [
    "model1 = Sequential()\n",
    "model1.add(Dense(64,activation='relu', input_dim=128))\n",
    "model1.add(Dense(36,activation='relu'))\n",
    "model1.add(Dropout(0.2))\n",
    "model1.add(Dense(16,activation='relu'))\n",
    "model1.add(Dropout(0.2))\n",
    "model1.add(Dense(6, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xaChB3NY9MYB",
    "outputId": "e44cc1f1-b4e2-4a5c-927a-71df74bb1dd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "252/252 [==============================] - 1s 1ms/step - loss: 1.6191 - accuracy: 0.3049\n",
      "Epoch 2/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.2101 - accuracy: 0.4016\n",
      "Epoch 3/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1552 - accuracy: 0.4302\n",
      "Epoch 4/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1401 - accuracy: 0.4365\n",
      "Epoch 5/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.0902 - accuracy: 0.4852\n",
      "Epoch 6/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.9809 - accuracy: 0.5560\n",
      "Epoch 7/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.8262 - accuracy: 0.6103\n",
      "Epoch 8/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.7595 - accuracy: 0.6307\n",
      "Epoch 9/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.7225 - accuracy: 0.6379\n",
      "Epoch 10/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6942 - accuracy: 0.6492\n",
      "Epoch 11/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6285 - accuracy: 0.6881\n",
      "Epoch 12/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6197 - accuracy: 0.6999\n",
      "Epoch 13/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6124 - accuracy: 0.7001\n",
      "Epoch 14/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5254 - accuracy: 0.7470\n",
      "Epoch 15/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4849 - accuracy: 0.7651\n",
      "Epoch 16/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4105 - accuracy: 0.7975\n",
      "Epoch 17/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4006 - accuracy: 0.7963\n",
      "Epoch 18/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3549 - accuracy: 0.8071\n",
      "Epoch 19/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3326 - accuracy: 0.8173\n",
      "Epoch 20/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3393 - accuracy: 0.8225\n",
      "Epoch 21/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3370 - accuracy: 0.8174\n",
      "Epoch 22/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3699 - accuracy: 0.8090\n",
      "Epoch 23/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5074 - accuracy: 0.7758\n",
      "Epoch 24/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3062 - accuracy: 0.8211\n",
      "Epoch 25/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3221 - accuracy: 0.8289\n",
      "Epoch 26/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3297 - accuracy: 0.8152\n",
      "Epoch 27/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3213 - accuracy: 0.8155\n",
      "Epoch 28/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3426 - accuracy: 0.8150\n",
      "Epoch 29/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3632 - accuracy: 0.8052\n",
      "Epoch 30/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3001 - accuracy: 0.8293\n",
      "Epoch 31/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3005 - accuracy: 0.8226\n",
      "Epoch 32/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3070 - accuracy: 0.8246\n",
      "Epoch 33/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3015 - accuracy: 0.8218\n",
      "Epoch 34/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3408 - accuracy: 0.8092\n",
      "Epoch 35/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2942 - accuracy: 0.8321\n",
      "Epoch 36/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3035 - accuracy: 0.8244\n",
      "Epoch 37/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3095 - accuracy: 0.8161\n",
      "Epoch 38/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3085 - accuracy: 0.8197\n",
      "Epoch 39/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3356 - accuracy: 0.8091\n",
      "Epoch 40/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3106 - accuracy: 0.8239\n",
      "Epoch 41/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2903 - accuracy: 0.8275\n",
      "Epoch 42/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2806 - accuracy: 0.8355\n",
      "Epoch 43/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2858 - accuracy: 0.8257\n",
      "Epoch 44/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2690 - accuracy: 0.8437\n",
      "Epoch 45/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3705 - accuracy: 0.8105\n",
      "Epoch 46/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2573 - accuracy: 0.8490\n",
      "Epoch 47/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2592 - accuracy: 0.8507\n",
      "Epoch 48/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2562 - accuracy: 0.8630\n",
      "Epoch 49/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2339 - accuracy: 0.8774\n",
      "Epoch 50/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2672 - accuracy: 0.8645\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fab046bad90>"
      ]
     },
     "execution_count": 89,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "model1.fit(scaler.transform(x_train),y_train1,batch_size=32,epochs=50,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G1WuH69a9YQ8",
    "outputId": "72beac49-15f6-4f12-83dc-04cecfb36530"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124/124 [==============================] - 0s 784us/step - loss: 0.0470 - accuracy: 0.9854\n",
      "\n",
      "accuracy: 98.54%\n"
     ]
    }
   ],
   "source": [
    "metrics=model1.evaluate(scaler.transform(x_test),np_utils.to_categorical(np.array(y_test),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (model1.metrics_names[1], metrics[1]*100))\n",
    "predictions = model1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FYicXZex9fcj"
   },
   "outputs": [],
   "source": [
    "model1.save('fifth_m.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ngENPQ1z-Ve6",
    "outputId": "be6a0309-6218-4a58-eb5d-d3072238076b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "252/252 [==============================] - 0s 852us/step - loss: 0.0456 - accuracy: 0.9859\n",
      "\n",
      "accuracy: 98.59%\n"
     ]
    }
   ],
   "source": [
    "metrics=model1.evaluate(scaler.transform(x_train),np_utils.to_categorical(np.array(y_train),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (model1.metrics_names[1], metrics[1]*100))\n",
    "predictions = model1.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sGnz6w-O_vgO"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "new_model = tf.keras.models.load_model('/content/third_m.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_3Lru28SQBUN",
    "outputId": "490700cc-0572-41f5-9cb4-ee9cd72cbd7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "252/252 [==============================] - 0s 1ms/step - loss: 0.0467 - accuracy: 0.9940\n",
      "\n",
      "accuracy: 99.40%\n"
     ]
    }
   ],
   "source": [
    "metrics=new_model.evaluate(scaler.transform(x_train),np_utils.to_categorical(np.array(y_train),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (new_model.metrics_names[1], metrics[1]*100))\n",
    "predictions = new_model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AzLzlUCPQg1o",
    "outputId": "cccb9171-120d-48d0-ba1e-3b4d4cd77b1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124/124 [==============================] - 0s 962us/step - loss: 0.0494 - accuracy: 0.9939\n",
      "\n",
      "accuracy: 99.39%\n"
     ]
    }
   ],
   "source": [
    "metrics=new_model.evaluate(scaler.transform(x_test),np_utils.to_categorical(np.array(y_test),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (new_model.metrics_names[1], metrics[1]*100))\n",
    "predictions = new_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gz3pxVyUQnS3",
    "outputId": "fe55bc12-f679-4cfa-a088-6a43bed8d82d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 72)                9288      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 36)                2628      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 36)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 16)                592       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 6)                 102       \n",
      "=================================================================\n",
      "Total params: 12,610\n",
      "Trainable params: 12,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cCCVoq8CQw7d",
    "outputId": "8598bdff-eb9b-4d73-9e83-1bc61ba73e43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8040, 128)\n"
     ]
    }
   ],
   "source": [
    "print(train_sc.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uqrNy18nRGVN"
   },
   "outputs": [],
   "source": [
    "pred_1=new_model.predict(sample_inp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UEJhX96XTOf4"
   },
   "outputs": [],
   "source": [
    "sample_inp=np.expand_dims(train_sc[0],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_fE5xSAIT0Q0",
    "outputId": "54797f4d-4635-4644-be1a-2b0cfe25f093"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 128)"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_inp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6qaiQslEUEWH",
    "outputId": "ef2b4f4c-d9fd-447c-82de-77882acc339d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "id": "oBcFipFfUcS6",
    "outputId": "462dc46e-a764-4e26-8888-a2cea2623e6f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6080</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8055</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9167</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9175</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11624</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       output\n",
       "6080        4\n",
       "8055        2\n",
       "9167        5\n",
       "9175        4\n",
       "11624       4"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UC0A4R02Uo2M"
   },
   "outputs": [],
   "source": [
    "new_t=scaler.transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aSf66uPhU8YN",
    "outputId": "26bfb877-7927-4d8e-9694-351d8436e340"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.86586419e-01, -4.56167078e-01, -3.13126420e-01, -1.66539887e-01,\n",
       "       -3.00327030e-02,  6.08315900e-02,  4.35452878e-02,  5.46623437e-02,\n",
       "        5.67100540e-02,  6.00079721e-02,  6.24646716e-02,  6.40899448e-02,\n",
       "        6.65357836e-02,  6.76174241e-02,  6.84622151e-02,  7.07694690e-02,\n",
       "        7.01856148e-02,  6.90723288e-02,  6.75449663e-02,  6.46530610e-02,\n",
       "        6.25103887e-02,  6.09571265e-02,  5.69579009e-02,  5.41823719e-02,\n",
       "        4.75149058e-02,  4.49225724e-02,  3.90996989e-02,  3.50241086e-02,\n",
       "        3.07511167e-02,  2.27553397e-02,  1.53635908e-02,  1.17196403e-02,\n",
       "        6.13557587e-03,  6.59869255e-04, -5.27414915e-03, -1.15807471e-02,\n",
       "       -1.77007971e-02, -2.20385774e-02, -2.70150840e-02, -3.01558983e-02,\n",
       "       -3.51174506e-02, -4.02097999e-02, -4.47513683e-02, -4.79862465e-02,\n",
       "       -5.13668755e-02, -5.31019423e-02, -5.47036759e-02, -5.53075089e-02,\n",
       "       -5.87733354e-02, -5.98639597e-02, -5.99727441e-02, -6.08190279e-02,\n",
       "       -5.86319887e-02, -6.01426580e-02, -5.70143518e-02, -5.46709692e-02,\n",
       "       -5.15007543e-02, -4.88877048e-02, -4.59143905e-02, -4.09569657e-02,\n",
       "       -3.59937857e-02, -3.18726067e-02, -2.62721202e-02, -2.17144171e-02,\n",
       "       -1.60562306e-02, -1.07634109e-02, -5.82621609e-03, -1.42182753e-03,\n",
       "        8.92389338e-03,  7.08924597e-02,  2.28035323e-01,  3.67630439e-01,\n",
       "        5.18091452e-01,  6.46831266e-01,  7.83066933e-01,  9.16924808e-01,\n",
       "        1.01490611e+00,  1.12108547e+00,  1.22270989e+00,  1.28246050e+00,\n",
       "        1.37071143e+00,  1.42628109e+00,  1.46075476e+00,  1.47566360e+00,\n",
       "        1.48876134e+00,  1.49073090e+00,  1.49136754e+00,  1.46910233e+00,\n",
       "        1.42239086e+00,  1.35779187e+00,  1.29119148e+00,  1.20034329e+00,\n",
       "        1.10947752e+00,  1.00156428e+00,  8.86494760e-01,  7.62607191e-01,\n",
       "        6.17877560e-01,  4.78568617e-01,  3.33792945e-01,  1.84998028e-01,\n",
       "        5.05414718e-02, -1.04149346e-01, -2.45822916e-01, -3.83290599e-01,\n",
       "       -5.26715857e-01, -6.59529128e-01, -7.93561402e-01, -9.24914543e-01,\n",
       "       -1.02297256e+00, -1.12773448e+00, -1.23120650e+00, -1.29516379e+00,\n",
       "       -1.36137755e+00, -1.42739325e+00, -1.45077249e+00, -1.47755494e+00,\n",
       "       -1.49713770e+00, -1.50099397e+00, -1.50729708e+00, -1.49571198e+00,\n",
       "       -1.47597196e+00, -1.42813663e+00, -1.36957493e+00, -1.30478430e+00,\n",
       "       -1.22194259e+00, -1.14672117e+00, -1.04834894e+00, -9.38420093e-01])"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_t[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "rg4VlJlPU_0P",
    "outputId": "e9073c2c-62fb-4198-e814-1775700bf211"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_sc[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "FSgfRW-lWNAT",
    "outputId": "bd2d2e58-b8ad-41cc-f59d-2f1b1b9c67ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_sc[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "J5vck_17WQ1c"
   },
   "outputs": [],
   "source": [
    "model2 = Sequential()\n",
    "model2.add(Dense(72,activation='relu',input_dim=128))\n",
    "model2.add(Dense(48,activation='relu'))\n",
    "model2.add(Dense(32,activation='relu'))\n",
    "model2.add(Dropout(0.3))\n",
    "model2.add(Dense(16,activation='relu'))\n",
    "model2.add(Dropout(0.2))\n",
    "model2.add(Dense(6, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HXC0sVsuZufj",
    "outputId": "4bd82a50-3746-4d59-e495-bc50fef03d2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "252/252 [==============================] - 1s 1ms/step - loss: 1.5938 - accuracy: 0.2921\n",
      "Epoch 2/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.2115 - accuracy: 0.3934\n",
      "Epoch 3/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1562 - accuracy: 0.4110\n",
      "Epoch 4/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1616 - accuracy: 0.4025\n",
      "Epoch 5/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.1343 - accuracy: 0.4320\n",
      "Epoch 6/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 1.0875 - accuracy: 0.4861\n",
      "Epoch 7/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.9582 - accuracy: 0.5979\n",
      "Epoch 8/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.8375 - accuracy: 0.6398\n",
      "Epoch 9/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.7095 - accuracy: 0.7035\n",
      "Epoch 10/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6477 - accuracy: 0.7222\n",
      "Epoch 11/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5973 - accuracy: 0.7395\n",
      "Epoch 12/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.6040 - accuracy: 0.7433\n",
      "Epoch 13/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5282 - accuracy: 0.7692\n",
      "Epoch 14/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.5228 - accuracy: 0.7683\n",
      "Epoch 15/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4809 - accuracy: 0.7853\n",
      "Epoch 16/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4822 - accuracy: 0.7855\n",
      "Epoch 17/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4317 - accuracy: 0.7951\n",
      "Epoch 18/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4388 - accuracy: 0.7951\n",
      "Epoch 19/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4594 - accuracy: 0.7889\n",
      "Epoch 20/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4217 - accuracy: 0.7945\n",
      "Epoch 21/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4346 - accuracy: 0.8018\n",
      "Epoch 22/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3972 - accuracy: 0.8029\n",
      "Epoch 23/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.4442 - accuracy: 0.7928\n",
      "Epoch 24/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3953 - accuracy: 0.8034\n",
      "Epoch 25/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3727 - accuracy: 0.8109\n",
      "Epoch 26/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3786 - accuracy: 0.8120\n",
      "Epoch 27/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3524 - accuracy: 0.8291\n",
      "Epoch 28/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.3401 - accuracy: 0.8708\n",
      "Epoch 29/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2955 - accuracy: 0.8889\n",
      "Epoch 30/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2999 - accuracy: 0.9049\n",
      "Epoch 31/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2406 - accuracy: 0.9204\n",
      "Epoch 32/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2242 - accuracy: 0.9282\n",
      "Epoch 33/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2293 - accuracy: 0.9271\n",
      "Epoch 34/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2238 - accuracy: 0.9333\n",
      "Epoch 35/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2366 - accuracy: 0.9342\n",
      "Epoch 36/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1934 - accuracy: 0.9432\n",
      "Epoch 37/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1836 - accuracy: 0.9504\n",
      "Epoch 38/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.2135 - accuracy: 0.9416\n",
      "Epoch 39/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1696 - accuracy: 0.9492\n",
      "Epoch 40/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1883 - accuracy: 0.9545\n",
      "Epoch 41/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1808 - accuracy: 0.9614\n",
      "Epoch 42/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1711 - accuracy: 0.9604\n",
      "Epoch 43/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1697 - accuracy: 0.9576\n",
      "Epoch 44/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1560 - accuracy: 0.9625\n",
      "Epoch 45/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1719 - accuracy: 0.9595\n",
      "Epoch 46/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1543 - accuracy: 0.9556\n",
      "Epoch 47/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1625 - accuracy: 0.9589\n",
      "Epoch 48/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1149 - accuracy: 0.9682\n",
      "Epoch 49/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1174 - accuracy: 0.9717\n",
      "Epoch 50/50\n",
      "252/252 [==============================] - 0s 1ms/step - loss: 0.1697 - accuracy: 0.9573\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7faafee35950>"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.compile(optimizer=\"RMSprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "model2.fit(scaler.transform(x_train),y_train1,batch_size=32,epochs=50,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Er8AXUVEcXjX",
    "outputId": "65f66f9d-6df4-4259-a183-42d53adc7e14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "252/252 [==============================] - 0s 820us/step - loss: 0.0142 - accuracy: 0.9979\n",
      "\n",
      "accuracy: 99.79%\n"
     ]
    }
   ],
   "source": [
    "metrics=model2.evaluate(scaler.transform(x_train),np_utils.to_categorical(np.array(y_train),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (model2.metrics_names[1], metrics[1]*100))\n",
    "predictions = model2.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PFm_nG8kkpdd",
    "outputId": "14c35a92-e79d-4e72-e335-c0aeca1d59e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124/124 [==============================] - 0s 858us/step - loss: 0.0156 - accuracy: 0.9970\n",
      "\n",
      "accuracy: 99.70%\n"
     ]
    }
   ],
   "source": [
    "metrics=model2.evaluate(scaler.transform(x_test),np_utils.to_categorical(np.array(y_test),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (model2.metrics_names[1], metrics[1]*100))\n",
    "predictions = model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HWp4WZeOkwLI"
   },
   "outputs": [],
   "source": [
    "model2.save('seventh_9.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4hErQBzzk3PT"
   },
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(data,out_data,test_size=0.33, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Po1cS8El0Po",
    "outputId": "d50fb40e-dea6-496b-d33e-ec263224762c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124/124 [==============================] - 0s 868us/step - loss: 0.0145 - accuracy: 0.9980\n",
      "\n",
      "accuracy: 99.80%\n"
     ]
    }
   ],
   "source": [
    "metrics=model2.evaluate(scaler.transform(x_test),np_utils.to_categorical(np.array(y_test),6),verbose=1)\n",
    "print()\n",
    "print(\"%s: %.2f%%\" % (model2.metrics_names[1], metrics[1]*100))\n",
    "predictions = model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0NcqPO9Dl5HZ"
   },
   "source": [
    "#### Final Accuracy Obtained for Test data: 99.80% \n",
    "#### Final Accuracy Obtained for Test data: 99.79%\n",
    "#### Total Parameters: 12,610"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Honeywell.ipynb",
   "provenance": []
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}