upskillproject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d93dc63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0969b9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            time_stamp  AmbientConditions.AmbientHumidity.U.Actual  \\\n",
      "0  2019-03-06 10:52:33                                       17.24   \n",
      "1  2019-03-06 10:52:34                                       17.24   \n",
      "2  2019-03-06 10:52:35                                       17.24   \n",
      "3  2019-03-06 10:52:36                                       17.24   \n",
      "4  2019-03-06 10:52:37                                       17.24   \n",
      "\n",
      "   AmbientConditions.AmbientTemperature.U.Actual  \\\n",
      "0                                          23.53   \n",
      "1                                          23.53   \n",
      "2                                          23.53   \n",
      "3                                          23.53   \n",
      "4                                          23.53   \n",
      "\n",
      "   Machine1.RawMaterial.Property1  Machine1.RawMaterial.Property2  \\\n",
      "0                           11.54                             200   \n",
      "1                           11.54                             200   \n",
      "2                           11.54                             200   \n",
      "3                           11.54                             200   \n",
      "4                           11.54                             200   \n",
      "\n",
      "   Machine1.RawMaterial.Property3  Machine1.RawMaterial.Property4  \\\n",
      "0                           963.0                             247   \n",
      "1                           963.0                             247   \n",
      "2                           963.0                             247   \n",
      "3                           963.0                             247   \n",
      "4                           963.0                             247   \n",
      "\n",
      "   Machine1.RawMaterialFeederParameter.U.Actual  \\\n",
      "0                                       1241.26   \n",
      "1                                       1246.09   \n",
      "2                                       1246.29   \n",
      "3                                       1247.59   \n",
      "4                                       1252.83   \n",
      "\n",
      "   Machine1.Zone1Temperature.C.Actual  Machine1.Zone2Temperature.C.Actual  \\\n",
      "0                                72.0                                72.3   \n",
      "1                                72.0                                72.3   \n",
      "2                                72.0                                72.3   \n",
      "3                                72.0                                72.3   \n",
      "4                                72.1                                72.4   \n",
      "\n",
      "   ...  Stage2.Output.Measurement10.U.Actual  \\\n",
      "0  ...                                   0.0   \n",
      "1  ...                                   0.0   \n",
      "2  ...                                   0.0   \n",
      "3  ...                                   0.0   \n",
      "4  ...                                   0.0   \n",
      "\n",
      "   Stage2.Output.Measurement10.U.Setpoint  \\\n",
      "0                                    7.93   \n",
      "1                                    7.93   \n",
      "2                                    7.93   \n",
      "3                                    7.93   \n",
      "4                                    7.93   \n",
      "\n",
      "   Stage2.Output.Measurement11.U.Actual  \\\n",
      "0                                   0.0   \n",
      "1                                   0.0   \n",
      "2                                   0.0   \n",
      "3                                   0.0   \n",
      "4                                   0.0   \n",
      "\n",
      "   Stage2.Output.Measurement11.U.Setpoint  \\\n",
      "0                                    5.65   \n",
      "1                                    5.65   \n",
      "2                                    5.65   \n",
      "3                                    5.65   \n",
      "4                                    5.65   \n",
      "\n",
      "   Stage2.Output.Measurement12.U.Actual  \\\n",
      "0                                   0.0   \n",
      "1                                   0.0   \n",
      "2                                   0.0   \n",
      "3                                   0.0   \n",
      "4                                   0.0   \n",
      "\n",
      "   Stage2.Output.Measurement12.U.Setpoint  \\\n",
      "0                                    1.85   \n",
      "1                                    1.85   \n",
      "2                                    1.85   \n",
      "3                                    1.85   \n",
      "4                                    1.85   \n",
      "\n",
      "   Stage2.Output.Measurement13.U.Actual  \\\n",
      "0                                   0.0   \n",
      "1                                   0.0   \n",
      "2                                   0.0   \n",
      "3                                   0.0   \n",
      "4                                   0.0   \n",
      "\n",
      "   Stage2.Output.Measurement13.U.Setpoint  \\\n",
      "0                                    2.89   \n",
      "1                                    2.89   \n",
      "2                                    2.89   \n",
      "3                                    2.89   \n",
      "4                                    2.89   \n",
      "\n",
      "   Stage2.Output.Measurement14.U.Actual  \\\n",
      "0                                   0.0   \n",
      "1                                   0.0   \n",
      "2                                   0.0   \n",
      "3                                   0.0   \n",
      "4                                   0.0   \n",
      "\n",
      "   Stage2.Output.Measurement14.U.Setpoint  \n",
      "0                                   11.71  \n",
      "1                                   11.71  \n",
      "2                                   11.71  \n",
      "3                                   11.71  \n",
      "4                                   11.71  \n",
      "\n",
      "[5 rows x 116 columns]\n",
      "(14088, 116)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data = pd.read_csv(\"continuous_factory_process.csv\")\n",
    "\n",
    "\n",
    "print(data.head())\n",
    "print(data.shape)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0529bd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time_stamp                                       0\n",
       "AmbientConditions.AmbientHumidity.U.Actual       0\n",
       "AmbientConditions.AmbientTemperature.U.Actual    0\n",
       "Machine1.RawMaterial.Property1                   0\n",
       "Machine1.RawMaterial.Property2                   0\n",
       "                                                ..\n",
       "Stage2.Output.Measurement12.U.Setpoint           0\n",
       "Stage2.Output.Measurement13.U.Actual             0\n",
       "Stage2.Output.Measurement13.U.Setpoint           0\n",
       "Stage2.Output.Measurement14.U.Actual             0\n",
       "Stage2.Output.Measurement14.U.Setpoint           0\n",
       "Length: 116, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "910d2695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AmbientConditions.AmbientHumidity.U.Actual</th>\n",
       "      <th>AmbientConditions.AmbientTemperature.U.Actual</th>\n",
       "      <th>Machine1.RawMaterial.Property1</th>\n",
       "      <th>Machine1.RawMaterial.Property2</th>\n",
       "      <th>Machine1.RawMaterial.Property3</th>\n",
       "      <th>Machine1.RawMaterial.Property4</th>\n",
       "      <th>Machine1.RawMaterialFeederParameter.U.Actual</th>\n",
       "      <th>Machine1.Zone1Temperature.C.Actual</th>\n",
       "      <th>Machine1.Zone2Temperature.C.Actual</th>\n",
       "      <th>Machine1.MotorAmperage.U.Actual</th>\n",
       "      <th>...</th>\n",
       "      <th>Stage2.Output.Measurement10.U.Actual</th>\n",
       "      <th>Stage2.Output.Measurement10.U.Setpoint</th>\n",
       "      <th>Stage2.Output.Measurement11.U.Actual</th>\n",
       "      <th>Stage2.Output.Measurement11.U.Setpoint</th>\n",
       "      <th>Stage2.Output.Measurement12.U.Actual</th>\n",
       "      <th>Stage2.Output.Measurement12.U.Setpoint</th>\n",
       "      <th>Stage2.Output.Measurement13.U.Actual</th>\n",
       "      <th>Stage2.Output.Measurement13.U.Setpoint</th>\n",
       "      <th>Stage2.Output.Measurement14.U.Actual</th>\n",
       "      <th>Stage2.Output.Measurement14.U.Setpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>1.408800e+04</td>\n",
       "      <td>14088.000000</td>\n",
       "      <td>1.408800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.330759</td>\n",
       "      <td>23.843635</td>\n",
       "      <td>11.851256</td>\n",
       "      <td>205.676320</td>\n",
       "      <td>951.679815</td>\n",
       "      <td>248.868896</td>\n",
       "      <td>1242.764276</td>\n",
       "      <td>72.013330</td>\n",
       "      <td>72.013106</td>\n",
       "      <td>70.333111</td>\n",
       "      <td>...</td>\n",
       "      <td>7.535958e+00</td>\n",
       "      <td>7.930000e+00</td>\n",
       "      <td>5.416515e+00</td>\n",
       "      <td>5.650000e+00</td>\n",
       "      <td>1.972561e+00</td>\n",
       "      <td>1.850000e+00</td>\n",
       "      <td>3.535251e+00</td>\n",
       "      <td>2.890000e+00</td>\n",
       "      <td>7.515574</td>\n",
       "      <td>1.171000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.188993</td>\n",
       "      <td>0.373535</td>\n",
       "      <td>0.510309</td>\n",
       "      <td>11.606324</td>\n",
       "      <td>126.662010</td>\n",
       "      <td>3.297820</td>\n",
       "      <td>95.845946</td>\n",
       "      <td>0.063183</td>\n",
       "      <td>0.406313</td>\n",
       "      <td>5.525217</td>\n",
       "      <td>...</td>\n",
       "      <td>1.645785e+00</td>\n",
       "      <td>2.730357e-12</td>\n",
       "      <td>1.182333e+00</td>\n",
       "      <td>1.820830e-13</td>\n",
       "      <td>4.145398e-01</td>\n",
       "      <td>3.790436e-13</td>\n",
       "      <td>4.768244e-01</td>\n",
       "      <td>4.361111e-13</td>\n",
       "      <td>2.082948</td>\n",
       "      <td>5.666779e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.840000</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>11.540000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>601.110000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>231.300000</td>\n",
       "      <td>71.900000</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.220420e-95</td>\n",
       "      <td>7.930000e+00</td>\n",
       "      <td>-2.703210e-95</td>\n",
       "      <td>5.650000e+00</td>\n",
       "      <td>-6.200020e-96</td>\n",
       "      <td>1.850000e+00</td>\n",
       "      <td>-1.822810e-95</td>\n",
       "      <td>2.890000e+00</td>\n",
       "      <td>-3.437021</td>\n",
       "      <td>1.171000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.040000</td>\n",
       "      <td>23.530000</td>\n",
       "      <td>11.540000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>1257.170000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>71.600000</td>\n",
       "      <td>68.880000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.840000e+00</td>\n",
       "      <td>7.930000e+00</td>\n",
       "      <td>5.560000e+00</td>\n",
       "      <td>5.650000e+00</td>\n",
       "      <td>2.030000e+00</td>\n",
       "      <td>1.850000e+00</td>\n",
       "      <td>3.450000e+00</td>\n",
       "      <td>2.890000e+00</td>\n",
       "      <td>7.720000</td>\n",
       "      <td>1.171000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.120000</td>\n",
       "      <td>23.930000</td>\n",
       "      <td>11.540000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>1264.440000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>71.980000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>7.930000e+00</td>\n",
       "      <td>5.630000e+00</td>\n",
       "      <td>5.650000e+00</td>\n",
       "      <td>2.060000e+00</td>\n",
       "      <td>1.850000e+00</td>\n",
       "      <td>3.510000e+00</td>\n",
       "      <td>2.890000e+00</td>\n",
       "      <td>7.870000</td>\n",
       "      <td>1.171000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.630000</td>\n",
       "      <td>24.160000</td>\n",
       "      <td>12.220000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>1027.430000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>1273.460000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.400000</td>\n",
       "      <td>72.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.950000e+00</td>\n",
       "      <td>7.930000e+00</td>\n",
       "      <td>5.800000e+00</td>\n",
       "      <td>5.650000e+00</td>\n",
       "      <td>2.090000e+00</td>\n",
       "      <td>1.850000e+00</td>\n",
       "      <td>3.760000e+00</td>\n",
       "      <td>2.890000e+00</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>1.171000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.240000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>1048.060000</td>\n",
       "      <td>257.000000</td>\n",
       "      <td>1331.820000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>72.700000</td>\n",
       "      <td>88.530000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.590000e+00</td>\n",
       "      <td>7.930000e+00</td>\n",
       "      <td>6.320000e+00</td>\n",
       "      <td>5.650000e+00</td>\n",
       "      <td>5.200000e+00</td>\n",
       "      <td>1.850000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.890000e+00</td>\n",
       "      <td>14.260000</td>\n",
       "      <td>1.171000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AmbientConditions.AmbientHumidity.U.Actual  \\\n",
       "count                                14088.000000   \n",
       "mean                                    15.330759   \n",
       "std                                      1.188993   \n",
       "min                                     13.840000   \n",
       "25%                                     14.040000   \n",
       "50%                                     15.120000   \n",
       "75%                                     16.630000   \n",
       "max                                     17.240000   \n",
       "\n",
       "       AmbientConditions.AmbientTemperature.U.Actual  \\\n",
       "count                                   14088.000000   \n",
       "mean                                       23.843635   \n",
       "std                                         0.373535   \n",
       "min                                        23.020000   \n",
       "25%                                        23.530000   \n",
       "50%                                        23.930000   \n",
       "75%                                        24.160000   \n",
       "max                                        24.430000   \n",
       "\n",
       "       Machine1.RawMaterial.Property1  Machine1.RawMaterial.Property2  \\\n",
       "count                    14088.000000                    14088.000000   \n",
       "mean                        11.851256                      205.676320   \n",
       "std                          0.510309                       11.606324   \n",
       "min                         11.540000                      200.000000   \n",
       "25%                         11.540000                      200.000000   \n",
       "50%                         11.540000                      200.000000   \n",
       "75%                         12.220000                      201.000000   \n",
       "max                         12.900000                      236.000000   \n",
       "\n",
       "       Machine1.RawMaterial.Property3  Machine1.RawMaterial.Property4  \\\n",
       "count                    14088.000000                    14088.000000   \n",
       "mean                       951.679815                      248.868896   \n",
       "std                        126.662010                        3.297820   \n",
       "min                        601.110000                      247.000000   \n",
       "25%                        963.000000                      247.000000   \n",
       "50%                        963.000000                      247.000000   \n",
       "75%                       1027.430000                      251.000000   \n",
       "max                       1048.060000                      257.000000   \n",
       "\n",
       "       Machine1.RawMaterialFeederParameter.U.Actual  \\\n",
       "count                                  14088.000000   \n",
       "mean                                    1242.764276   \n",
       "std                                       95.845946   \n",
       "min                                      231.300000   \n",
       "25%                                     1257.170000   \n",
       "50%                                     1264.440000   \n",
       "75%                                     1273.460000   \n",
       "max                                     1331.820000   \n",
       "\n",
       "       Machine1.Zone1Temperature.C.Actual  Machine1.Zone2Temperature.C.Actual  \\\n",
       "count                        14088.000000                        14088.000000   \n",
       "mean                            72.013330                           72.013106   \n",
       "std                              0.063183                            0.406313   \n",
       "min                             71.900000                           71.300000   \n",
       "25%                             72.000000                           71.600000   \n",
       "50%                             72.000000                           72.000000   \n",
       "75%                             72.000000                           72.400000   \n",
       "max                             72.500000                           72.700000   \n",
       "\n",
       "       Machine1.MotorAmperage.U.Actual  ...  \\\n",
       "count                     14088.000000  ...   \n",
       "mean                         70.333111  ...   \n",
       "std                           5.525217  ...   \n",
       "min                          44.400000  ...   \n",
       "25%                          68.880000  ...   \n",
       "50%                          71.980000  ...   \n",
       "75%                          72.920000  ...   \n",
       "max                          88.530000  ...   \n",
       "\n",
       "       Stage2.Output.Measurement10.U.Actual  \\\n",
       "count                          1.408800e+04   \n",
       "mean                           7.535958e+00   \n",
       "std                            1.645785e+00   \n",
       "min                           -5.220420e-95   \n",
       "25%                            7.840000e+00   \n",
       "50%                            7.900000e+00   \n",
       "75%                            7.950000e+00   \n",
       "max                            8.590000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement10.U.Setpoint  \\\n",
       "count                            1.408800e+04   \n",
       "mean                             7.930000e+00   \n",
       "std                              2.730357e-12   \n",
       "min                              7.930000e+00   \n",
       "25%                              7.930000e+00   \n",
       "50%                              7.930000e+00   \n",
       "75%                              7.930000e+00   \n",
       "max                              7.930000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement11.U.Actual  \\\n",
       "count                          1.408800e+04   \n",
       "mean                           5.416515e+00   \n",
       "std                            1.182333e+00   \n",
       "min                           -2.703210e-95   \n",
       "25%                            5.560000e+00   \n",
       "50%                            5.630000e+00   \n",
       "75%                            5.800000e+00   \n",
       "max                            6.320000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement11.U.Setpoint  \\\n",
       "count                            1.408800e+04   \n",
       "mean                             5.650000e+00   \n",
       "std                              1.820830e-13   \n",
       "min                              5.650000e+00   \n",
       "25%                              5.650000e+00   \n",
       "50%                              5.650000e+00   \n",
       "75%                              5.650000e+00   \n",
       "max                              5.650000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement12.U.Actual  \\\n",
       "count                          1.408800e+04   \n",
       "mean                           1.972561e+00   \n",
       "std                            4.145398e-01   \n",
       "min                           -6.200020e-96   \n",
       "25%                            2.030000e+00   \n",
       "50%                            2.060000e+00   \n",
       "75%                            2.090000e+00   \n",
       "max                            5.200000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement12.U.Setpoint  \\\n",
       "count                            1.408800e+04   \n",
       "mean                             1.850000e+00   \n",
       "std                              3.790436e-13   \n",
       "min                              1.850000e+00   \n",
       "25%                              1.850000e+00   \n",
       "50%                              1.850000e+00   \n",
       "75%                              1.850000e+00   \n",
       "max                              1.850000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement13.U.Actual  \\\n",
       "count                          1.408800e+04   \n",
       "mean                           3.535251e+00   \n",
       "std                            4.768244e-01   \n",
       "min                           -1.822810e-95   \n",
       "25%                            3.450000e+00   \n",
       "50%                            3.510000e+00   \n",
       "75%                            3.760000e+00   \n",
       "max                            8.000000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement13.U.Setpoint  \\\n",
       "count                            1.408800e+04   \n",
       "mean                             2.890000e+00   \n",
       "std                              4.361111e-13   \n",
       "min                              2.890000e+00   \n",
       "25%                              2.890000e+00   \n",
       "50%                              2.890000e+00   \n",
       "75%                              2.890000e+00   \n",
       "max                              2.890000e+00   \n",
       "\n",
       "       Stage2.Output.Measurement14.U.Actual  \\\n",
       "count                          14088.000000   \n",
       "mean                               7.515574   \n",
       "std                                2.082948   \n",
       "min                               -3.437021   \n",
       "25%                                7.720000   \n",
       "50%                                7.870000   \n",
       "75%                                8.080000   \n",
       "max                               14.260000   \n",
       "\n",
       "       Stage2.Output.Measurement14.U.Setpoint  \n",
       "count                            1.408800e+04  \n",
       "mean                             1.171000e+01  \n",
       "std                              5.666779e-13  \n",
       "min                              1.171000e+01  \n",
       "25%                              1.171000e+01  \n",
       "50%                              1.171000e+01  \n",
       "75%                              1.171000e+01  \n",
       "max                              1.171000e+01  \n",
       "\n",
       "[8 rows x 115 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "847026d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data = data.drop(columns=['time_stamp'])\n",
    "data.isnull().sum() \n",
    "data = data.dropna()  \n",
    "\n",
    "\n",
    "X = data.drop(columns=['Stage1.Output.Measurement0.U.Actual','Stage1.Output.Measurement0.U.Setpoint','Stage1.Output.Measurement1.U.Actual','Stage1.Output.Measurement1.U.Setpoint','Stage1.Output.Measurement2.U.Actual','Stage1.Output.Measurement2.U.Setpoint','Stage1.Output.Measurement3.U.Actual','Stage1.Output.Measurement3.U.Setpoint','Stage1.Output.Measurement4.U.Actual','Stage1.Output.Measurement4.U.Setpoint','Stage1.Output.Measurement5.U.Actual','Stage1.Output.Measurement5.U.Setpoint','Stage1.Output.Measurement6.U.Actual','Stage1.Output.Measurement6.U.Setpoint','Stage1.Output.Measurement7.U.Actual','Stage1.Output.Measurement7.U.Setpoint','Stage1.Output.Measurement8.U.Actual','Stage1.Output.Measurement8.U.Setpoint','Stage1.Output.Measurement9.U.Actual','Stage1.Output.Measurement9.U.Setpoint','Stage1.Output.Measurement10.U.Actual','Stage1.Output.Measurement10.U.Setpoint','Stage1.Output.Measurement11.U.Actual','Stage1.Output.Measurement11.U.Setpoint','Stage1.Output.Measurement12.U.Actual','Stage1.Output.Measurement12.U.Setpoint','Stage1.Output.Measurement13.U.Actual','Stage1.Output.Measurement13.U.Setpoint','Stage1.Output.Measurement14.U.Actual','Stage1.Output.Measurement14.U.Setpoint',\n",
    "'Machine4.Temperature1.C.Actual','Machine4.Temperature2.C.Actual','Machine4.Pressure.C.Actual','Machine4.Temperature3.C.Actual','Machine4.Temperature4.C.Actual','Machine4.Temperature5.C.Actual','Machine4.ExitTemperature.U.Actual','Machine5.Temperature1.C.Actual','Machine5.Temperature2.C.Actual','Machine5.Temperature3.C.Actual','Machine5.Temperature4.C.Actual','Machine5.Temperature5.C.Actual','Machine5.Temperature6.C.Actual','Machine5.ExitTemperature.U.Actual','Stage2.Output.Measurement0.U.Actual','Stage2.Output.Measurement0.U.Setpoint','Stage2.Output.Measurement1.U.Actual','Stage2.Output.Measurement1.U.Setpoint','Stage2.Output.Measurement2.U.Actual','Stage2.Output.Measurement2.U.Setpoint','Stage2.Output.Measurement3.U.Actual','Stage2.Output.Measurement3.U.Setpoint','Stage2.Output.Measurement4.U.Actual','Stage2.Output.Measurement4.U.Setpoint','Stage2.Output.Measurement5.U.Actual','Stage2.Output.Measurement5.U.Setpoint','Stage2.Output.Measurement6.U.Actual','Stage2.Output.Measurement6.U.Setpoint','Stage2.Output.Measurement7.U.Actual','Stage2.Output.Measurement7.U.Setpoint','Stage2.Output.Measurement8.U.Actual','Stage2.Output.Measurement8.U.Setpoint','Stage2.Output.Measurement9.U.Actual','Stage2.Output.Measurement9.U.Setpoint','Stage2.Output.Measurement10.U.Actual','Stage2.Output.Measurement10.U.Setpoint','Stage2.Output.Measurement11.U.Actual','Stage2.Output.Measurement11.U.Setpoint','Stage2.Output.Measurement12.U.Actual','Stage2.Output.Measurement12.U.Setpoint','Stage2.Output.Measurement13.U.Actual','Stage2.Output.Measurement13.U.Setpoint','Stage2.Output.Measurement14.U.Actual','Stage2.Output.Measurement14.U.Setpoint'])\n",
    "y1 = data['Stage1.Output.Measurement0.U.Actual']\n",
    "y2 = data['Stage1.Output.Measurement0.U.Setpoint']\n",
    "y3 = data['Stage1.Output.Measurement1.U.Setpoint']\n",
    "y4 = data['Stage1.Output.Measurement2.U.Actual']\n",
    "y5 = data['Stage1.Output.Measurement2.U.Setpoint']\n",
    "\n",
    "X_train, X_test, y1_train, y1_test,y2_train, y2_test,y3_train, y3_test,y4_train, y4_test,y5_train, y5_test = train_test_split(X, y1,y2,y3,y4,y5, test_size=0.18, random_state=35)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb632167",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_regressor = RandomForestRegressor() \n",
    "\n",
    "\n",
    "multioutput_regressor = MultiOutputRegressor(rf_regressor)\n",
    "y_test_combined = np.column_stack((y1_test, y2_test, y3_test, y4_test, y5_test))\n",
    "y_train_combined = np.column_stack((y1_train, y2_train, y3_train, y4_train, y5_train))\n",
    "\n",
    "multioutput_regressor.fit(X_train, y_train_combined)\n",
    "y_pred_combined = multioutput_regressor.predict(X_test)\n",
    "y_pred = np.hsplit(y_pred_combined, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96d7bbf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8012110767298045"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multioutput_regressor.score(X_test,y_test_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9cac62b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.2299990277173011\n",
      "RMSE: 0.4795821386554144\n",
      "R-squared: 0.8012110767298045\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Evaluate ensemble performance\n",
    "mse = mean_squared_error(y_test_combined, y_pred_combined)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test_combined, y_pred_combined)\n",
    "\n",
    "print(\"MSE:\", mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "701f58f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_combined = np.column_stack((y1_test, y2_test, y3_test, y4_test, y5_test))\n",
    "\n",
    "y_pred_combined = multioutput_regressor.predict(X_test)\n",
    "y_pred = np.hsplit(y_pred_combined, 5)\n",
    "\n",
    "residuals = y_test_combined - y_pred_combined\n",
    "plt.scatter(y_pred_combined, residuals)\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot')\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93b62ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr_matrix = data.corr()\n",
    "\n",
    "# Plot the correlation heatmap\n",
    "sns.heatmap(corr_matrix, cmap='coolwarm', annot=True)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b621670f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = data.drop(columns=['Machine1.RawMaterial.Property1','Machine1.RawMaterial.Property2','Machine1.RawMaterial.Property3','Machine1.RawMaterial.Property4','Machine1.RawMaterialFeederParameter.U.Actual','Machine1.Zone1Temperature.C.Actual','Machine1.Zone2Temperature.C.Actual','Machine1.MotorAmperage.U.Actual','Machine1.MotorRPM.C.Actual','Machine1.MaterialPressure.U.Actual','Machine1.MaterialTemperature.U.Actual','Machine1.ExitZoneTemperature.C.Actual','Machine2.RawMaterial.Property1','Machine2.RawMaterial.Property2','Machine2.RawMaterial.Property3','Machine2.RawMaterial.Property4','Machine2.RawMaterialFeederParameter.U.Actual','Machine2.Zone1Temperature.C.Actual','Machine2.Zone2Temperature.C.Actual','Machine2.MotorAmperage.U.Actual','Machine2.MotorRPM.C.Actual','Machine2.MaterialPressure.U.Actual','Machine2.MaterialTemperature.U.Actual','Machine2.ExitZoneTemperature.C.Actual','Machine3.RawMaterial.Property1','Machine3.RawMaterial.Property2','Machine3.RawMaterial.Property3','Machine3.RawMaterial.Property4','Machine3.RawMaterialFeederParameter.U.Actual','Machine3.Zone1Temperature.C.Actual','Machine3.Zone2Temperature.C.Actual','Machine3.MotorAmperage.U.Actual','Machine3.MotorRPM.C.Actual','Machine3.MaterialPressure.U.Actual','Machine3.MaterialTemperature.U.Actual','Machine3.ExitZoneTemperature.C.Actual','FirstStage.CombinerOperation.Temperature1.U.Actual','FirstStage.CombinerOperation.Temperature2.U.Actual','FirstStage.CombinerOperation.Temperature3.C.Actual','Stage1.Output.Measurement0.U.Actual','Stage1.Output.Measurement0.U.Setpoint','Stage1.Output.Measurement1.U.Actual','Stage1.Output.Measurement1.U.Setpoint','Stage1.Output.Measurement2.U.Actual','Stage1.Output.Measurement2.U.Setpoint','Stage1.Output.Measurement3.U.Actual','Stage1.Output.Measurement3.U.Setpoint','Stage1.Output.Measurement4.U.Actual','Stage1.Output.Measurement4.U.Setpoint','Stage1.Output.Measurement5.U.Actual','Stage1.Output.Measurement5.U.Setpoint','Stage1.Output.Measurement6.U.Actual','Stage1.Output.Measurement6.U.Setpoint','Stage1.Output.Measurement7.U.Actual','Stage1.Output.Measurement7.U.Setpoint','Stage1.Output.Measurement8.U.Actual','Stage1.Output.Measurement8.U.Setpoint','Stage1.Output.Measurement9.U.Actual','Stage1.Output.Measurement9.U.Setpoint','Stage1.Output.Measurement10.U.Actual','Stage1.Output.Measurement10.U.Setpoint','Stage1.Output.Measurement11.U.Actual','Stage1.Output.Measurement11.U.Setpoint','Stage1.Output.Measurement12.U.Actual','Stage1.Output.Measurement12.U.Setpoint','Stage1.Output.Measurement13.U.Actual','Stage1.Output.Measurement13.U.Setpoint','Stage1.Output.Measurement14.U.Actual','Stage1.Output.Measurement14.U.Setpoint','Stage2.Output.Measurement0.U.Actual','Stage2.Output.Measurement0.U.Setpoint','Stage2.Output.Measurement1.U.Actual','Stage2.Output.Measurement1.U.Setpoint','Stage2.Output.Measurement2.U.Actual','Stage2.Output.Measurement2.U.Setpoint','Stage2.Output.Measurement3.U.Actual','Stage2.Output.Measurement3.U.Setpoint','Stage2.Output.Measurement4.U.Actual','Stage2.Output.Measurement4.U.Setpoint','Stage2.Output.Measurement5.U.Actual','Stage2.Output.Measurement5.U.Setpoint','Stage2.Output.Measurement6.U.Actual','Stage2.Output.Measurement6.U.Setpoint','Stage2.Output.Measurement7.U.Actual','Stage2.Output.Measurement7.U.Setpoint','Stage2.Output.Measurement8.U.Actual','Stage2.Output.Measurement8.U.Setpoint','Stage2.Output.Measurement9.U.Actual','Stage2.Output.Measurement9.U.Setpoint','Stage2.Output.Measurement10.U.Actual','Stage2.Output.Measurement10.U.Setpoint','Stage2.Output.Measurement11.U.Actual','Stage2.Output.Measurement11.U.Setpoint','Stage2.Output.Measurement12.U.Actual','Stage2.Output.Measurement12.U.Setpoint','Stage2.Output.Measurement13.U.Actual','Stage2.Output.Measurement13.U.Setpoint','Stage2.Output.Measurement14.U.Actual','Stage2.Output.Measurement14.U.Setpoint'])\n",
    "\n",
    "data.isnull().sum() \n",
    "data = data.dropna()  \n",
    "\n",
    "y6 = data['Stage2.Output.Measurement0.U.Setpoint']\n",
    "y7 = data['Stage2.Output.Measurement1.U.Setpoint']\n",
    "y8 = data['Stage2.Output.Measurement2.U.Setpoint']\n",
    "y9 = data['Stage2.Output.Measurement3.U.Setpoint']\n",
    "y10 = data['Stage2.Output.Measurement4.U.Setpoint']\n",
    "\n",
    "\n",
    "\n",
    "X1_train,X1_test,y6_train,y6_test,y7_train,y7_test ,y8_train,y8_test ,y9_train,y9_test ,y10_train,y10_test  = train_test_split(X1 , y6,y7,y8,y9,y10 , test_size=0.7,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3018d9ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svr1 = SVR()\n",
    "svr2 = SVR()\n",
    "svr3 = SVR()\n",
    "svr4 = SVR()\n",
    "svr5 = SVR()\n",
    "\n",
    "svr1.fit(X1_train, y6_train)\n",
    "svr2.fit(X1_train, y7_train)\n",
    "svr3.fit(X1_train, y8_train)\n",
    "svr4.fit(X1_train, y9_train)\n",
    "svr5.fit(X1_train, y10_train)\n",
    "\n",
    "y6_pred = svr1.predict(X1_test)\n",
    "y7_pred = svr2.predict(X1_test)\n",
    "y8_pred = svr3.predict(X1_test)\n",
    "y9_pred = svr4.predict(X1_test)\n",
    "y10_pred = svr5.predict(X1_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b296f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Variable 1:\n",
      "Mean Squared Error: 0.0\n",
      "Mean Absolute Error: 0.0\n",
      "R-squared Score: 1.0\n",
      "\n",
      "Target Variable 2:\n",
      "Mean Squared Error: 0.0\n",
      "Mean Absolute Error: 0.0\n",
      "R-squared Score: 1.0\n",
      "\n",
      "Target Variable 3:\n",
      "Mean Squared Error: 0.0\n",
      "Mean Absolute Error: 0.0\n",
      "R-squared Score: 1.0\n",
      "\n",
      "Target Variable 4:\n",
      "Mean Squared Error: 0.0\n",
      "Mean Absolute Error: 0.0\n",
      "R-squared Score: 1.0\n",
      "\n",
      "Target Variable 5:\n",
      "Mean Squared Error: 0.0\n",
      "Mean Absolute Error: 0.0\n",
      "R-squared Score: 1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "mse1 = mean_squared_error(y6_test, y6_pred)\n",
    "mae1 = mean_absolute_error(y6_test, y6_pred)\n",
    "r2_1 = r2_score(y6_test, y6_pred)\n",
    "\n",
    "mse2 = mean_squared_error(y7_test, y7_pred)\n",
    "mae2 = mean_absolute_error(y7_test, y7_pred)\n",
    "r2_2 = r2_score(y7_test, y7_pred)\n",
    "\n",
    "mse3 = mean_squared_error(y8_test, y8_pred)\n",
    "mae3 = mean_absolute_error(y8_test, y8_pred)\n",
    "r2_3 = r2_score(y8_test, y8_pred)\n",
    "\n",
    "mse4 = mean_squared_error(y9_test, y9_pred)\n",
    "mae4 = mean_absolute_error(y9_test, y9_pred)\n",
    "r2_4 = r2_score(y9_test, y9_pred)\n",
    "\n",
    "mse5 = mean_squared_error(y10_test, y10_pred)\n",
    "mae5 = mean_absolute_error(y10_test, y10_pred)\n",
    "r2_5 = r2_score(y10_test, y10_pred)\n",
    "\n",
    "print(\"Target Variable 1:\")\n",
    "print(\"Mean Squared Error:\", mse1)\n",
    "print(\"Mean Absolute Error:\", mae1)\n",
    "print(\"R-squared Score:\", r2_1)\n",
    "print()\n",
    "\n",
    "print(\"Target Variable 2:\")\n",
    "print(\"Mean Squared Error:\", mse2)\n",
    "print(\"Mean Absolute Error:\", mae2)\n",
    "print(\"R-squared Score:\", r2_2)\n",
    "print()\n",
    "\n",
    "print(\"Target Variable 3:\")\n",
    "print(\"Mean Squared Error:\", mse3)\n",
    "print(\"Mean Absolute Error:\", mae3)\n",
    "print(\"R-squared Score:\", r2_3)\n",
    "print()\n",
    "\n",
    "print(\"Target Variable 4:\")\n",
    "print(\"Mean Squared Error:\", mse4)\n",
    "print(\"Mean Absolute Error:\", mae4)\n",
    "print(\"R-squared Score:\", r2_4)\n",
    "print()\n",
    "\n",
    "print(\"Target Variable 5:\")\n",
    "print(\"Mean Squared Error:\", mse5)\n",
    "print(\"Mean Absolute Error:\", mae5)\n",
    "print(\"R-squared Score:\", r2_5)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a89f393",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf1_regressor = RandomForestRegressor(n_estimators=100) \n",
    "\n",
    "multioutput1_regressor = MultiOutputRegressor(rf1_regressor)\n",
    "y12_test_combined = np.column_stack((y6_test, y7_test, y8_test, y9_test, y10_test))\n",
    "y12_train_combined = np.column_stack((y6_train, y7_train, y8_train, y9_train, y10_train))\n",
    "\n",
    "\n",
    "multioutput1_regressor.fit(X1_train,y12_train_combined)\n",
    "y12_pred_combined = multioutput1_regressor.predict(X1_test)\n",
    "y12_pred = np.hsplit(y12_pred_combined, y12_pred_combined.shape[1])\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6add7714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905189418293023"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multioutput1_regressor.score(X1_test,y12_test_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d686c547",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found input variables with inconsistent numbers of samples:\n",
      "Array 1: 4226 samples\n",
      "Array 2: 9862 samples\n",
      "Array 3: 4226 samples\n",
      "Array 4: 9862 samples\n",
      "Array 5: 4226 samples\n",
      "Array 6: 9862 samples\n",
      "Array 7: 4226 samples\n",
      "Array 8: 9862 samples\n",
      "Array 9: 4226 samples\n",
      "Array 10: 9862 samples\n",
      "Array 11: 4226 samples\n",
      "Array 12: 9862 samples\n"
     ]
    }
   ],
   "source": [
    "array_lengths = [len(X1_train), len(X1_test), len(y6_train), len(y6_test), len(y7_train), len(y7_test), len(y8_train), len(y8_test), len(y9_train), len(y9_test), len(y10_train), len(y10_test)]\n",
    "\n",
    "if len(set(array_lengths)) != 1:\n",
    "    print(\"Found input variables with inconsistent numbers of samples:\")\n",
    "    for i, length in enumerate(array_lengths):\n",
    "        print(f\"Array {i+1}: {length} samples\")\n",
    "else:\n",
    "    print(\"Input arrays have consistent numbers of samples.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ece295f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77aba4b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "models = []\n",
    "num_models = 5\n",
    "\n",
    "for _ in range(num_models):\n",
    "    rf_model = RandomForestRegressor(n_estimators=100, max_depth=None)\n",
    "    models.append(rf_model)\n",
    "\n",
    "for model in models:\n",
    "    model.fit(X_train_scaled, y_train_combined)\n",
    "\n",
    "y_pred_combined = np.mean([model.predict(X_test_scaled) for model in models], axis=0)\n",
    "y_pred = np.hsplit(y_pred_combined, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6671e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.1761250141685595\n",
      "RMSE: 0.4196725082353614\n",
      "R-squared: 0.8440479303408435\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Evaluate ensemble performance\n",
    "mse = mean_squared_error(y_test_combined, y_pred_combined)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test_combined, y_pred_combined)\n",
    "\n",
    "print(\"MSE:\", mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "146e6e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction on New Data:\n",
      "Prediction for y1: [[9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]]\n",
      "Prediction for y2: [[13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]\n",
      " [13.3375]]\n",
      "Prediction for y3: [[22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]\n",
      " [22.0578]]\n",
      "Prediction for y4: [[8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
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      " [8.61124]\n",
      " [8.61124]]\n",
      "Prediction for y5: [[12.6294]\n",
      " [12.6294]\n",
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      " [12.6294]\n",
      " [12.6294]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Assuming 'new_data' has the same features as the original 'data' DataFrame\n",
    "num_samples = 100\n",
    "num_features = X.shape[1]  # Replace X with your original features\n",
    "\n",
    "# Generate synthetic new_data\n",
    "new_data = pd.DataFrame(np.random.rand(num_samples, num_features), columns=X.columns)\n",
    "\n",
    "# Scale the new_data using the same scaler used for training\n",
    "new_data_scaled = scaler.transform(new_data)\n",
    "\n",
    "# Make predictions on new data using the ensemble models\n",
    "new_data_pred_combined = np.mean([model.predict(new_data_scaled) for model in models], axis=0)\n",
    "new_data_pred = np.hsplit(new_data_pred_combined, 5)\n",
    "\n",
    "# Print the predictions on new data\n",
    "print(\"Prediction on New Data:\")\n",
    "for i, y_pred in enumerate(new_data_pred):\n",
    "    print(f\"Prediction for y{i+1}: {y_pred}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0821c6e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess new data\n",
    "new_data_scaled = scaler.transform(new_data)\n",
    "\n",
    "# Make predictions on new data\n",
    "new_data_pred_combined = np.mean([model.predict(new_data_scaled) for model in models], axis=0)\n",
    "new_data_pred = np.hsplit(new_data_pred_combined, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "700bd4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the trained model\n",
    "joblib.dump(models, \"ensemble_model.pkl\")\n",
    "\n",
    "# Load the trained model\n",
    "loaded_models = joblib.load(\"ensemble_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0cee6df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction on New Data:\n",
      "Prediction for y1: [[9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
      " [9.63072]\n",
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      " [9.63072]\n",
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      " [9.63072]\n",
      " [9.63072]]\n",
      "Prediction for y2: [[13.3375]\n",
      " [13.3375]\n",
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      " [13.3375]]\n",
      "Prediction for y3: [[22.0578]\n",
      " [22.0578]\n",
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      " [22.0578]]\n",
      "Prediction for y4: [[8.61124]\n",
      " [8.61124]\n",
      " [8.61124]\n",
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      " [8.61124]]\n",
      "Prediction for y5: [[12.6294]\n",
      " [12.6294]\n",
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      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]\n",
      " [12.6294]]\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Load the trained model\n",
    "loaded_models = joblib.load(\"ensemble_model.pkl\")\n",
    "\n",
    "# Assuming you have a new dataset in a DataFrame called 'new_data'\n",
    "new_data_scaled = scaler.transform(new_data)  # Scale the new data using the same scaler used for training\n",
    "\n",
    "# Make predictions on new data using the loaded models\n",
    "new_data_pred_combined = np.mean([model.predict(new_data_scaled) for model in loaded_models], axis=0)\n",
    "new_data_pred_combined = new_data_pred_combined.reshape(-1, 5)  # Reshape to (num_samples, num_outputs)\n",
    "new_data_pred = np.hsplit(new_data_pred_combined, 5)\n",
    "\n",
    "# Print the predictions on new data\n",
    "print(\"Prediction on New Data:\")\n",
    "for i, y_pred in enumerate(new_data_pred):\n",
    "    print(f\"Prediction for y{i+1}: {y_pred}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c3b01ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have the actual values for y1 and y2\n",
    "y1_actual = data['Stage1.Output.Measurement0.U.Actual']\n",
    "y2_actual = data['Stage1.Output.Measurement0.U.Setpoint']\n",
    "\n",
    "# Scatter plot of predicted values for y1 and y2\n",
    "plt.scatter(new_data_pred[0], new_data_pred[1], color='red', label='Predicted')\n",
    "plt.scatter(y1_actual, y2_actual, color='blue', label='Actual')\n",
    "plt.xlabel('y1')\n",
    "plt.ylabel('y2')\n",
    "plt.legend()\n",
    "plt.title('Scatter Plot: Predicted vs Actual')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d36485b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of predicted values for y1\n",
    "plt.hist(new_data_pred[0], bins=10, color='red', alpha=0.5, label='Predicted')\n",
    "plt.xlabel('y1')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.title('Histogram: Predicted y1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "130768e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m4MCBJiwszJQoUcKUL1/eREVFmVdffdU+z9SpU01UVJQpV66c8fT0NJUqVTKDBg0yhw4dyvP+Aq7EZowxTklkAAAARQxjjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAS7G/wWNWVpaOHTumgICAHL9LBQAAiiZjjM6fP6/Q0FC5uRVeP06xD0bHjh1TeHi4s8sAAAC34fDhw4Xyw9HZin0wCggIkHT1hS1ZsqSTqwEAAHmRkpKi8PBw++d4YSn2wSj79FnJkiUJRgAAuJjCHgbD4GsAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIxcrQoUMVHR2toUOHOrsUAIALIhih2EhKStKuXbskSbt27VJSUpKTKwIAuBqCEYqNZ5555qbPAQC4FYIRioXFixfr4sWLDtMuXryoxYsXO6kiAIArIhjB5WVkZGju3Lm5ts2dO1cZGRmFXBEAwFURjODyFi5ceEftAABkIxjB5fXr1++O2gEAyEYwgsvz8PDQ008/nWvbs88+Kw8Pj0KuCADgqghGKBZ69+4tX19fh2m+vr7q2bOnkyoCALgighGKjXfeeeemzwEAuBWCEYqNSpUqqV69epKkevXqqVKlSk6uCADgahh8gWJl1qxZzi4BAODC6DECAACwEIwAAAAsBCMAAACLU4PR5MmT1aRJEwUEBCgoKEhdu3bV3r17HeYxxig2NlahoaHy8fFRdHS0fv75ZydVDAAAijOnBqMNGzZoyJAh2rp1q9asWaOMjAy1a9dOFy5csM8zZcoUTZs2TbNmzVJCQoJCQkLUtm1bnT9/3omVAwCA4shmjDHOLiLbqVOnFBQUpA0bNqhly5Yyxig0NFTDhw/X888/L0lKT09XcHCwXn/99Rve7fhaKSkpCgwMVHJyskqWLHm3dwEAABQAZ31+F6kxRsnJyZKkMmXKSJISExN14sQJtWvXzj6Pl5eXWrVqpc2bN+e6jvT0dKWkpDg8AAAA8qLIBCNjjEaOHKkHHnjAfpO+EydOSJKCg4Md5g0ODra3XW/y5MkKDAy0P8LDw+9u4QAAoNgoMsFo6NCh+umnn7R48eIcbTabzeG5MSbHtGxjx45VcnKy/XH48OG7Ui8AACh+isSdr//yl79oxYoV2rhxoypWrGifHhISIulqz1GFChXs00+ePJmjFymbl5eXvLy87m7BAACgWHJqj5ExRkOHDtXSpUu1bt06RUZGOrRHRkYqJCREa9assU+7fPmyNmzYoKioqMIuFwAAFHNO7TEaMmSIFi1apH/9618KCAiwjxsKDAyUj4+PbDabhg8frkmTJql69eqqXr26Jk2aJF9fX8XExDizdAAAUAw5NRjNmTNHkhQdHe0wPS4uTgMGDJAkjRkzRpcuXdJzzz2ns2fPqmnTpvr6668VEBBQyNUCAIDirkjdx+hu4D5GAAC4Hu5jBAAA4GQEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMUK5s3b1bPnj21efNmZ5cCAHBBBCMUG2lpaZo2bZp+//13TZs2TWlpac4uCQDgYghGKDY+/PBDnTlzRpJ05swZLVq0yMkVAQBcDcEIxcKRI0e0aNEiGWMkScYYLVq0SEeOHHFyZQAAV0IwgsszxmjmzJk3nJ4dlgAAuBWCEVxeUlKSEhISlJmZ6TA9MzNTCQkJSkpKclJlAABXQzCCy6tUqZKaNGkid3d3h+nu7u667777VKlSJSdVBgBwNQQjuDybzaZhw4bdcLrNZnNCVQAAV0QwQrFQsWJFxcTE2EOQzWZTTEyMwsLCnFwZAMCVEIxQbPTp00dly5aVJJUrV04xMTFOrggA4GoIRig2vL29FRISIkkKDg6Wt7e3kysCALgaghGKjd9//127du2SJO3atUu///67kysCALgaghGKjeeee87h+ZAhQ5xUCQDAVRGMUCx89dVX9p8DyXb69Gl99dVXTqoIAOCKCEZweZmZmXr99ddzbXv99ddz3PgRAIAbIRjB5a1YseKGP/thjNGKFSsKuSIAgKsiGMHllS9f/o7aAQDIRjCCy4uKirrhpfk+Pj6Kiooq5IoAAK6KYASX5+bmpuHDh+faNnz4cLm58TYHAOQNnxhwecYYrV27Nte2b7755objjwAAuB7BCC4vKSlJCQkJubYlJCQoKSmpkCsCALgqghFcXqVKldSkSRO5u7s7THd3d9d9992nSpUqOakyAICrIRjB5dlsNg0bNuyG0202mxOqAgC4IoIRioWKFSsqJibGHoJsNptiYmIUFhbm5MoAAK6EYIRio0+fPipbtqwkqVy5coqJiXFyRQAAV0MwQrHh7e2tkSNHKjg4WCNGjLjhvY0AALgRD2cXABSkqKgobugIALht9BgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWJwajDZu3KguXbooNDRUNptNy5cvd2gfMGCAbDabw6NZs2bOKRYAABR7Tg1GFy5cUMOGDTVr1qwbztOhQwcdP37c/vjiiy8KsUIAAPDfxMOZG+/YsaM6dux403m8vLwUEhJSSBUBAID/ZkV+jFF8fLyCgoJUo0YNDR48WCdPnnR2SQAAoJhyao/RrXTs2FFPPPGEIiIilJiYqAkTJujBBx/Ujz/+KC8vr1yXSU9PV3p6uv15SkpKYZULAABcXJEORj179rT/f7169dS4cWNFRERo1apV6t69e67LTJ48WS+99FJhlQgAAIqRIn8q7VoVKlRQRESE9u/ff8N5xo4dq+TkZPvj8OHDhVghAABwZUW6x+h6Z86c0eHDh1WhQoUbzuPl5XXD02wAAAA349RglJqaqgMHDtifJyYmaseOHSpTpozKlCmj2NhYPfbYY6pQoYIOHTqkcePGqVy5curWrZsTqwYAAMWVU4PRDz/8oNatW9ufjxw5UpLUv39/zZkzR//5z3+0cOFCnTt3ThUqVFDr1q21ZMkSBQQEOKtkAABQjNmMMcbZRdxNKSkpCgwMVHJyskqWLOnscgAAQB446/PbpQZfAwAA3E0EIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALB4OLsAACiqjDFKS0tTWlqas0u5bVlZWUpJSXF2GchFyZIl5ebmuv0T3t7e8vb2ls1mc3YpBYpghGIlNjZW8fHxio6OVmxsrLPLgYtLS0tTx44dnV0GUGR9+eWX8vHxcXYZBcp1oypwnd9//13x8fGSpPj4eP3+++/OLQgA4HJsxhjj7CLuppSUFAUGBio5OVklS5Z0djm4i7p06aLz58/bnwcEBOjzzz93YkVwdZxKw93EqbSbc9bnN6fSUCx89dVXDqFIks6fP6+vvvpKHTp0cFJVcHU2m00+Pj4uf6qgbNmyzi4BcBmuG1UBS2Zmpl577bVc21577TVlZmYWckUAAFdFMILL+/TTT++oHQCAbAQjuLxPPvnkjtoBAMjGGCPYB5i6qoYNG2rt2rU3bb906VIhVlRwiuM9QgCgKOOqNOjSpUvcq6WIKo73CAGAvHDW5zen0gAAACycSrtDrn4aSrq6D8uWLXN2GXese/fuurYD1GazaenSpU6s6M4ZY1z2NGA2TgcCcCUEozt06dIlderUydllIBfGGHXr1s3ZZfzX++KLL+Tr6+vsMgAgTziVdofS09OdXQJQpHGMAHAlBCMAAAALwegOeXl5ObsEoEjjGAHgShhjdId8fHz05ZdfOrsMSLnecoC/jfN5e3s7uwQAyDOC0R3K/pFJV1YcrqwrrriiCwAKF8EISktLK7Y3eHT1/eIGjwBQuBhjBAAAYKHHCPL29nbpsTh56RVy1f1jfA4AFC6CEYrFOKlbKe77BwAoGJxKg8urVKnSHbUDAJCNYASX16RJkztqBwAgG8EILm///v131A4AQDaCEVxeyZIl76gdAIBsBCO4vHbt2t1ROwAA2QhGcHlRUVF31A4AQDaCEVxeQkLCHbUDAJCNYASX17Rp0xuOIwoMDFTTpk0LuSIAgKsiGMHlubm56cUXX8y1beLEiXJz420OAMgbPjFQLDRu3Fj169d3mNagQQM1atTISRUBAFwRwQjFxiuvvGLvHXJzc9PLL7/s5IoAAK6GYIRio1SpUurTp4/c3NzUp08flSpVytklAQBcjM0YY5xdxN2UkpKiwMBAJScnc6M/AABchLM+v+kxAgAAsBCMAAAALAQjAAAAy20Fo4yMDH3zzTeaO3euzp8/L0k6duyYUlNTC7Q4AACAwuSR3wV+++03dejQQUlJSUpPT1fbtm0VEBCgKVOmKC0tTe+8887dqBMAAOCuy3eP0bBhw9S4cWOdPXtWPj4+9undunXT2rVrC7Q4AACAwpTvHqNvv/1W3333nTw9PR2mR0RE6OjRowVWGAAAQGHLd49RVlaWMjMzc0w/cuSIAgICCqQoAAAAZ8h3MGrbtq1mzJhhf26z2ZSamqqJEyeqU6dOBVkbAABAocr3na+PHTum1q1by93dXfv371fjxo21f/9+lStXThs3blRQUNDdqvW2cOdrAABcj7M+v/M9xig0NFQ7duzQ4sWL9e9//1tZWVkaNGiQ+vTp4zAYGwAAwNXwW2kAAKDIcZkeo4ULF960vV+/frddDAAAgDPlu8eodOnSDs+vXLmiixcvytPTU76+vvrjjz8KtMA7RY8RAACux1mf3/m+Ku3s2bMOj9TUVO3du1cPPPCAFi9efDdqBAAAKBQF8iOy1atX12uvvaZhw4YVxOoAAACcokCCkSS5u7vr2LFjBbU6AACAQpfvwdcrVqxweG6M0fHjxzVr1izdf//9BVYYAABAYct3MOratavDc5vNpvLly+vBBx/U1KlTC6ouAACAQpfvYJSVlXU36gAAAHC6AhtjBAAA4Ory1GM0cuTIPK9w2rRpt10MAACAM+UpGG3fvj1PK7PZbHdUDAAAgDPlKRitX7/+rmx848aNeuONN/Tjjz/q+PHjWrZsmcPgbmOMXnrpJb377rs6e/asmjZtqtmzZ6tu3bp3pR4AAPDfzaljjC5cuKCGDRtq1qxZubZPmTJF06ZN06xZs5SQkKCQkBC1bdtW58+fL+RKAQDAf4N8X5UmSQkJCfrkk0+UlJSky5cvO7QtXbo0z+vp2LGjOnbsmGubMUYzZszQ+PHj1b17d0nSggULFBwcrEWLFunpp5++ndIBAABuKN89Rh999JHuv/9+7d69W8uWLdOVK1e0e/durVu3ToGBgQVWWGJiok6cOKF27drZp3l5ealVq1bavHnzDZdLT09XSkqKwwMAACAv8h2MJk2apOnTp2vlypXy9PTUzJkztWfPHvXo0UOVKlUqsMJOnDghSQoODnaYHhwcbG/LzeTJkxUYGGh/hIeHF1hNAACgeMt3MDp48KA6d+4s6WoPzoULF2Sz2TRixAi9++67BV7g9Ve6GWNuevXb2LFjlZycbH8cPny4wGsCAADFU76DUZkyZeyDn8PCwrRr1y5J0rlz53Tx4sUCKywkJESScvQOnTx5Mkcv0rW8vLxUsmRJhwcAAEBe5DkY7dixQ5LUokULrVmzRpLUo0cPDRs2TIMHD1bv3r3Vpk2bAissMjJSISEh9m1J0uXLl7VhwwZFRUUV2HYAAACy5fmqtEaNGulPf/qTunbtqt69e0u6etqqRIkS+vbbb9W9e3dNmDAhXxtPTU3VgQMH7M8TExO1Y8cOlSlTRpUqVdLw4cM1adIkVa9eXdWrV9ekSZPk6+urmJiYfG0HAAAgL2zGGJOXGbds2aJ58+bp448/1pUrV9S9e3cNGjRIrVu3vu2Nx8fH57p8//79NX/+fPsNHufOnetwg8d69erleRspKSkKDAxUcnIyp9UAAHARzvr8znMwynbp0iV9/PHHiouL06ZNm1S5cmUNHDhQ/fv3V8WKFe9WnbeNYAQAgOtx1ud3vgdf+/j4qH///oqPj9e+ffvUu3dvzZ07V5GRkerUqdPdqBEAAKBQ5LvH6Hqpqan68MMPNW7cOJ07d06ZmZkFVVuBoMcIAADX46zP79v6SRBJ2rBhg+bNm6fPPvtM7u7u6tGjhwYNGlSQtQEAABSqfAWjw4cPa/78+Zo/f74SExMVFRWlt956Sz169JCfn9/dqhEAAKBQ5DkYtW3bVuvXr1f58uXVr18/DRw4UDVr1rybtQEAABSqPAcjHx8fffbZZ3r44Yfl7u5+N2sCAABwijwHoxUrVtzNOgAAAJwu35frAwAAFFcEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAEuRDkaxsbGy2WwOj5CQEGeXBQAAiikPZxdwK3Xr1tU333xjf+7u7u7EagAAQHFW5IORh4cHvUQAAKBQFOlTaZK0f/9+hYaGKjIyUr169dKvv/560/nT09OVkpLi8AAAAMiLIh2MmjZtqoULF2r16tV67733dOLECUVFRenMmTM3XGby5MkKDAy0P8LDwwuxYgAA4Mpsxhjj7CLy6sKFC6patarGjBmjkSNH5jpPenq60tPT7c9TUlIUHh6u5ORklSxZsrBKBQAAdyAlJUWBgYGF/vld5McYXcvPz0/169fX/v37bziPl5eXvLy8CrEqAABQXBTpU2nXS09P1549e1ShQgVnlwIAAIqhIh2MRo0apQ0bNigxMVHbtm3T448/rpSUFPXv39/ZpQEAgGKoSJ9KO3LkiHr37q3Tp0+rfPnyatasmbZu3aqIiAhnlwYAAIqhIh2MPvroI2eXAAAA/osU6VNpAAAAhYlgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAIDFw9kFAAUpOjra/v/x8fFOqwNAweP4RmFwiR6jt99+W5GRkfL29ta9996rTZs2ObskFEHX/qOZ23MArqtz5843fQ4UlCIfjJYsWaLhw4dr/Pjx2r59u1q0aKGOHTsqKSnJ2aUBAArJhQsXbvocKCg2Y4xxdhE307RpUzVq1Ehz5syxT6tdu7a6du2qyZMn33L5lJQUBQYGKjk5WSVLlrybpcKJbtY7RJc74No4vv87Oevzu0j3GF2+fFk//vij2rVr5zC9Xbt22rx5c67LpKenKyUlxeGB4u3rr7++o3YARdfWrVvvqB3IryIdjE6fPq3MzEwFBwc7TA8ODtaJEydyXWby5MkKDAy0P8LDwwujVDjRpEmT7qgdQNH197///Y7agfwq0sEom81mc3hujMkxLdvYsWOVnJxsfxw+fLgwSoQTjRs37o7aARRdr7322h21A/lVpINRuXLl5O7unqN36OTJkzl6kbJ5eXmpZMmSDg8Ub9efas1vO4Ciq1mzZnfUDuRXkQ5Gnp6euvfee7VmzRqH6WvWrFFUVJSTqkJRdKMBmAzMBFwfxzcKU5G/wePIkSP15JNPqnHjxmrevLneffddJSUl6ZlnnnF2aQCAQuLn5+dwib6fn58Tq0FxVuSDUc+ePXXmzBm9/PLLOn78uOrVq6cvvvhCERERzi4NRUx8fDx3xgWKqVWrVjkc36tWrXJeMSjWivx9jO4U9zECAMD1cB8jAAAAJyMYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAp8j8Jcqeyb+ydkpLi5EoAAEBeZX9uF/YPdBT7YHT+/HlJUnh4uJMrAQAA+XX+/HkFBgYW2vaK/W+lZWVl6dixYwoICJDNZnN2ObjLUlJSFB4ersOHD/PbeEAxw/H938UYo/Pnzys0NFRuboU38qfY9xi5ubmpYsWKzi4DhaxkyZL8wwkUUxzf/z0Ks6coG4OvAQAALAQjAAAAC8EIxYqXl5cmTpwoLy8vZ5cCoIBxfKMwFPvB1wAAAHlFjxEAAICFYAQAAGAhGAEAAFgIRgCKvejoaA0fPtzZZQC4TfPnz1epUqUKZVsuG4xOnjypp59+WpUqVZKXl5dCQkLUvn17bdmyRZJks9m0fPlyp9R2/PhxxcTEqGbNmnJzc8vXP8gLFizQfffdJz8/PwUEBKhly5ZauXJlvmsYMGCAunbtmu/l8iI2Nlb33HNPnuaz2Wzq0KFDjrYpU6bIZrMpOjq64Av8L1O5cmXNmDEjx/T//Oc/atWqlXx8fBQWFqaXX375pr85dOjQIdlsNu3YsSNHW9euXTVgwIBb1nLp0iWVLl1aZcqU0aVLl2467/XHsI+Pj4KCgjiGLRzD/z2KwjGcmZmpyZMnq1atWvLx8VGZMmXUrFkzxcXF5Xk/4uPjZbPZdO7cuTwvk1c9e/bUvn378rXM7X4hctlg9Nhjj2nnzp1asGCB9u3bpxUrVig6Olp//PGHs0tTenq6ypcvr/Hjx6thw4Z5Xm7UqFF6+umn1aNHD+3cuVPff/+9WrRooUcffVSzZs26ixXfPRUqVND69et15MgRh+lxcXGqVKmSk6rKu8uXLzu7hNuSkpKitm3bKjQ0VAkJCXrrrbf05ptvatq0aXd1u5999pnq1aunOnXqaOnSpTed9/pj+MEHH1S5cuU4hosYjmHnKOxjODY2VjNmzNArr7yi3bt3a/369Ro8eLDOnj17V7aXX9lfnAqFcUFnz541kkx8fHyu7REREUaS/REREWGMMebAgQPmkUceMUFBQcbPz880btzYrFmzxmHZY8eOmU6dOhlvb29TuXJl8+GHH5qIiAgzffp0+zznzp0zgwcPNuXLlzcBAQGmdevWZseOHbnW0qpVKzNs2LBb7tOWLVuMJPPPf/4zR9vIkSNNiRIlTFJSkjHGmIkTJ5qGDRs6zDN9+nT7fk6cONFh/yWZ9evXm8TERCPJLF682DRv3tx4eXmZOnXqmPXr19vXExcXZwIDAx3WvWzZMpP9VomLi8ux7ri4uFz3KbvOhx9+2Lz66qv26d99950pV66cefbZZ02rVq0clpk3b56pVauW8fLyMjVr1jSzZ892aB8zZoypXr268fHxMZGRkeaFF14wly9ftrfv2LHDREdHG39/fxMQEGAaNWpkEhIS8vS6GWNM//79zaOPPmomTZpkKlSoYG87cuSI6dGjhylVqpQpU6aMeeSRR0xiYmKO5f7xj3+YoKAgExgYaGJjY82VK1fMqFGjTOnSpU1YWJh5//33Hbaf1/W+8cYbJiQkxJQpU8Y899xz9n1u1apVjr+HMca8/fbbJjAw0KSlpdnXNXnyZBMaGmqysrJy/Xtlvz+2b9+eo+3RRx81/fv3z3W5a0VHR5t33nnHzJkzx7Ru3TpH+65du0ynTp2Mv7+/kWTq169vDhw4kOt79tpHeHi4MebqMZy9z76+vqZx48bms88+M7169TJhYWHGx8fH1KxZ09xzzz0Ox7CXl5dp2bKlvQ6OYY5hjmFHDRs2NLGxsTdsN8aYrKws8/rrr5vIyEjj7e1tGjRoYD755BOHbV/7yN5eq1atzJAhQ8yQIUNMYGCgKVOmjBk/frzDfvzxxx/mySefNKVKlTI+Pj6mQ4cOZt++ffb269/X2e+FhQsXmoiICFOyZEnTs2dPk5KSYoy5+rpfX8+1f5ebcckeI39/f/n7+2v58uVKT0/P0Z6QkCDp6jea48eP25+npqaqU6dO+uabb7R9+3a1b99eXbp0UVJSkn3Zfv366dixY4qPj9dnn32md999VydPnrS3G2PUuXNnnThxQl988YV+/PFHNWrUSG3atLmjb7qLFy+Wv7+/nn766Rxtf/vb33TlyhV99tlneVrXqFGj1KNHD3Xo0EHHjx/X8ePHFRUVZW8fPXq0/va3v2n79u2KiorSI488ojNnzuRp3T179tTf/vY31a1b177unj173nSZgQMHav78+fbn8+bNU58+feTp6ekw33vvvafx48frH//4h/bs2aNJkyZpwoQJWrBggX2egIAAzZ8/X7t379bMmTP13nvvafr06fb2Pn36qGLFikpISNCPP/6ov//97ypRokSe9i3b2rVrtWfPHq1Zs0YrV67UxYsX1bp1a/n7+2vjxo369ttv5e/vrw4dOjh8G123bp2OHTumjRs3atq0aYqNjdXDDz+s0qVLa9u2bXrmmWf0zDPP6PDhw5KU5/WuX79eBw8e1Pr167VgwQLNnz/f/nouXbpUFStW1Msvv2z/e0jSli1b1KpVK4cb4bVv317Hjh3ToUOH8vV65NXBgwe1ZcsW9ejRQz169NDmzZv166+/2tuPHj2qli1bytvbW2vWrJGvr69CQkJ08eLFHO/Z//znP5Kk559/3v7aSleP4QceeECStHLlSrVv314xMTGqUqWKVq5cqV27dkmSduzYoVmzZtmP4StXrtjr4BjmGOYYzikkJETr1q3TqVOnbjjPCy+8oLi4OM2ZM0c///yzRowYob59+2rDhg0KDw+3v7/37t2r48ePa+bMmfZlFyxYIA8PD23btk3//Oc/NX36dP3v//6vvX3AgAH64YcftGLFCm3ZskXGGHXq1Mnh2L3ewYMHtXz5cq1cuVIrV67Uhg0b9Nprr0mSZs6cqebNm2vw4MH21zU8PDxvL0ae4lMR9Omnn5rSpUsbb29vExUVZcaOHWt27txpb5dkli1bdsv11KlTx7z11lvGGGP27NljJNm/nRhjzP79+40ke4/R2rVrTcmSJR1SvDHGVK1a1cydOzfH+vP6bbNDhw45vgldKzAw0Dz77LPGmPx9a7pWdqJ/7bXX7NOuXLliKlasaF5//XVjzK2/bd5o+7nJnu/y5csmKCjIbNiwwaSmppqAgACzc+dOM2zYMIdvm+Hh4WbRokUO63jllVdM8+bNb7iNKVOmmHvvvdf+PCAgwMyfP/+m9Vwrt9ctODjYpKen26e9//77pmbNmg7fbtLT042Pj49ZvXq1fbmIiAiTmZlpn6dmzZqmRYsW9ucZGRnGz8/PLF68ON/rzcjIsM/zxBNPmJ49e9qfX9+jaYwxbdu2NYMHD3aYdvToUSPJbN68OdfX5057jMaNG2e6du3qsMz48ePtz8eOHWsiIyPt35SvP4br169voqOj7fNLMq+88oqRZM6ePWufvn37dodvf7kdw1FRUeZvf/ubMeb/H8PZPUYcwxzDxnAMX+/nn382tWvXNm5ubqZ+/frm6aefNl988YW9PTU11Xh7e+fY9qBBg0zv3r2NMcasX78+x/FqzNVjqHbt2g6v0/PPP29q165tjDFm3759RpL57rvv7O2nT582Pj4+5uOPPzbG5N5j5Ovra+8hMsaY0aNHm6ZNmzpsNy/H7vVcssdIujo+4dixY1qxYoXat2+v+Ph4NWrUyOFbzfUuXLigMWPGqE6dOipVqpT8/f31yy+/2HuM9u7dKw8PDzVq1Mi+TLVq1VS6dGn78x9//FGpqakqW7asvefK399fiYmJOnjw4F3bX2OMbDZbgayrefPm9v/38PBQ48aNtWfPngJZd25KlCihvn37Ki4uTp988olq1KihBg0aOMxz6tQpHT58WIMGDXJ4XV999VWH1/XTTz/VAw88oJCQEPn7+2vChAkOPX4jR47UU089pYceekivvfbabf1N6tev7/BN+Mcff9SBAwcUEBBgr6tMmTJKS0tzWH/dunXl5vb/D6ng4GDVr1/f/tzd3V1ly5a190DmZ73u7u725xUqVHDoxbyR698vxhq0WVDvo2tlZmZqwYIF6tu3r31a3759tWDBAmVmZkq62ovTokUL+7f/64/hEydOaMOGDbc8hrMHqTZo0MB+DC9ZskQNGjRQkyZNJEnbtm2zvy+qVasmDw8P+zo4hvOPY/iq4nwM16lTR7t27dLWrVv1P//zP/r999/VpUsXPfXUU5Kk3bt3Ky0tTW3btnX4+y5cuDBPf6NmzZo51N28eXPt379fmZmZ2rNnjzw8PNS0aVN7e9myZVWzZs2bvq8rV66sgIAA+/O8vq634nHrWYoub29vtW3bVm3bttWLL76op556ShMnTrzhyPvRo0dr9erVevPNN1WtWjX5+Pjo8ccft3d5Zr/prnft9KysLFWoUEHx8fE55ruTSwlr1Kihb7/9VpcvX87RPX3s2DGlpKSoevXqkiQ3N7cctd6suzEvst+wd2Pd0tWu+KZNm2rXrl0aOHBgjvasrCxJV7virz04JNn/Qdm6dat69eqll156Se3bt1dgYKA++ugjTZ061T5vbGysYmJitGrVKn355ZeaOHGiPvroI3Xr1i3P++bn55ejtnvvvVcffvhhjnnLly9v///ru/ttNluu07L39U7Wm72OGwkJCdGJEyccpmX/gxEcHJzrMoGBgZKk5OTkHG3nzp1TRETEDbe3evVqHT16NMcpmczMTH399dfq2LGjfHx8cix37TH866+/av369Q7HcPb7MvvvNnr0aH3zzTeSpI8//ljVqlVTy5Yt9cMPP+i9997TqVOnNGbMGLVr1+6Gg245hm8Px/D/n1Ycj2Hp6nunSZMmatKkiUaMGKEPPvhATz75pMaPH2+vd9WqVQoLC3NY7k5/u+5mn703C4G387rmhcv2GOWmTp06unDhgqSrL1j2N9VsmzZt0oABA9StWzfVr19fISEhDudqa9WqpYyMDG3fvt0+7cCBAw6XHjZq1EgnTpyQh4eHqlWr5vAoV67cbdfeq1cvpaamau7cuTna3nzzTZUoUUKPPfaYpKsH3IkTJxzeTNdfnunp6Zlj/7Nt3brV/v8ZGRn68ccfVatWLfu6z58/b38d87vuG6lbt67q1q2rXbt2KSYmJkd7cHCwwsLC9Ouvv+Z4XSMjIyVJ3333nSIiIjR+/Hg1btxY1atX12+//ZZjXTVq1NCIESP09ddfq3v37vbLTfPyuuWmUaNG2r9/v4KCgnLUlv0P0e0oqPXm9vdo3ry5Nm7c6BAOvv76a4WGhqpy5cq5rqd06dIqX768fUxetkuXLunnn39WzZo1b1jD+++/r169emnHjh0Ojz59+uj999+XdLWHZ9OmTTf8kPb09JS/v7/DMezv7y9J9nEXmzZt0r333ivp6vEaEhKiU6dOqUqVKurbt686duyojIwM+1gj6eoxnJGRYX/OMcwxzDGcN3Xq1JF0tae2Tp068vLyUlJSUo59zR67k/2FILf31rXv2ezn1atXl7u7u+rUqaOMjAxt27bN3n7mzBnt27dPtWvXzlfN17qd97nkosHozJkzevDBB/XBBx/op59+UmJioj755BNNmTJFjz76qKSrXWxr167ViRMn7JcbVqtWTUuXLtWOHTu0c+dOxcTEOKTLWrVq6aGHHtKf//xnff/999q+fbv+/Oc/y8fHx55aH3roITVv3lxdu3bV6tWrdejQIW3evFkvvPCCfvjhB/u6sj8YUlNTderUKe3YsUO7d++2ty9btsz+D5l09SAYNmyYRo8eralTp+rgwYP65Zdf9MILL2jmzJmaOnWq/c0XHR2tU6dOacqUKTp48KBmz56tL7/80uE1qly5sn766Sft3btXp0+fdvgwmj17tpYtW6ZffvlFQ4YM0dmzZ+3fAJs2bSpfX1+NGzdOBw4c0KJFi3Kc2qhcubISExO1Y8cOnT592j4Avl+/fho7duwN/27r1q3T8ePHb/itPDY2VpMnT9bMmTO1b98+/ec//1FcXJz98tRq1aopKSlJH330kQ4ePKh//vOfWrZsmX35S5cuaejQoYqPj9dvv/2m7777TgkJCfYDKy+vW2769OmjcuXK6dFHH9WmTZuUmJioDRs2aNiwYTkuYc6Pglpv5cqVtXHjRh09elSnT5+WJMXExMjLy0sDBgzQrl27tGzZMk2aNEkjR460v5e///571apVS0ePHrWva9SoUZo0aZL+7//+TwcPHtQPP/ygfv36ycPDw+E02dixY9WvXz9JV0+hfP755+rfv7/q1avn8Ojfv79WrFihU6dOaejQoUpJSVGvXr30zTffqHnz5nrmmWe0YsUKJSYm6vz58/rll18UHR2t06dPKyIiQnv27FFoaKjGjRunffv2yd/fX6tXr5Z0tWs/JiZGbm5uSkpK0ubNm2WMUWhoqI4cOaKzZ8/aj+FrT49wDHMMcww7HsOS9Pjjj2v69Onatm2bfvvtN8XHx2vIkCGqUaOGatWqpYCAAI0aNUojRozQggULdPDgQW3fvl2zZ8+2D66PiIiQzWbTypUrderUKaWmptrXf/jwYY0cOVJ79+7V4sWL9dZbb2nYsGGSpOrVq+vRRx/V4MGD9e2332rnzp3q27evwsLC7J/pt6Ny5cratm2bDh06pNOnT+e9Nynfo5KKgLS0NPP3v//dNGrUyAQGBhpfX19Ts2ZN88ILL5iLFy8aY4xZsWKFqVatmvHw8LAPzEtMTDStW7c2Pj4+Jjw83MyaNSvH4Kxjx46Zjh07Gi8vLxMREWEWLVpkgoKCzDvvvGOfJyUlxfzlL38xoaGhpkSJEiY8PNz06dPHfimuMSbXS46vHSCYfcns9d5//33TuHFj4+PjY3x9fc0DDzxgVqxYkWO+OXPmmPDwcOPn52f69etn/vGPfzis/+TJk6Zt27b2y6KvvdR30aJFpmnTpsbT09PUrl3brF271mHdy5YtM9WqVTPe3t7m4YcfNu+++65DrWlpaeaxxx4zpUqVcrjUt1WrVg6D+241wPP6gZvGGPPhhx+ae+65x3h6eprSpUubli1bmqVLl9rbR48ebcqWLWv8/f1Nz549zfTp0+0D8tLT002vXr1MeHi48fT0NKGhoWbo0KHm0qVLeX7dchvwaowxx48fN/369TPlypUzXl5epkqVKmbw4MEmOTn5hsvlNvDv+kGWt7Pe61+3LVu2mAYNGhgvLy+Hv9NPP/1kWrRoYby8vExISIiJjY11GPyYPVDy2ktYMzMzzezZs02DBg2Mn5+fCQsLM4899pjZv3+/Qw39+/e31/Dmm2+aUqVKOVxyne3KlSumTJkyZurUqcYYY3bu3GnatWtnfHx8jKenp/2SbF9fX1OtWjUTGRlp/Pz8jCTzj3/8w1SrVs24u7ubEiVKGG9vb9OkSRNTt25dI8mEhoaaWbNmmfvvv99UqVLF+Pv7m6CgIDNs2DATFhZm3Nzc7MdwiRIlzIMPPmivi2OYY5hjuL9DDe+++65p3bq1KV++vPH09DSVKlUyAwYMMIcOHbLPk5WVZWbOnGlq1qxpSpQoYcqXL2/at29vNmzYYJ/n5ZdfNiEhIcZmszlcrv/cc8+ZZ555xpQsWdKULl3a/P3vf8/1cv3AwEDj4+Nj2rdvn6fL9a91/UD8vXv3mmbNmhkfH598Xa5vM+Ymt9GEjhw5ovDwcH3zzTdq06aNs8u5I4cOHVJkZKS2b9+ep7veAsUBxzDgXNHR0brnnntyvbt3UeTSg6/vhnXr1ik1NVX169fX8ePHNWbMGFWuXFktW7Z0dmkA8oBjGMCdIBhd58qVKxo3bpx+/fVXBQQEKCoqSh9++GG+bzAGwDk4hgHcCU6lAQAAWFzyqjQAAIC7gWAEAABgIRgBAABYCEYAAAAWghEAl2Gz2bR8+fI8zx8bG3vL+/0MGDBAXbt2vaO6ABQfBCMABapLly566KGHcm3bsmWLbDab/v3vf9/Wuo8fP66OHTveSXkAcFMEIwAFatCgQVq3bl2uPw46b9483XPPPWrUqFG+1pn9Q5ohISF3/EveAHAzBCMABerhhx9WUFBQjh8uvXjxopYsWaKuXbuqd+/eqlixonx9fVW/fn0tXrzYYd7o6GgNHTpUI0eOVLly5dS2bVtJOU+lPf/886pRo4Z8fX1VpUoVTZgwweHHVrPNnTtX4eHh8vX11RNPPKFz587dsH5jjKZMmaIqVarIx8dHDRs21KeffnrbrwcA10IwAlCgPDw81K9fP82fP1/X3j/2k08+0eXLl/XUU0/p3nvv1cqVK7Vr1y79+c9/1pNPPqlt27Y5rGfBggXy8PDQd999p7lz5+a6rYCAAM2fP1+7d+/WzJkz9d5772n69OkO8xw4cEAff/yxPv/8c3311VfasWOHhgwZcsP6X3jhBcXFxWnOnDn6+eefNWLECPXt21cbNmy4g1cFgKvgztcACtwvv/yi2rVra926dWrdurUkqVWrVgoLC9OiRYtyzN+5c2fVrl1bb775pqSrPUbJycnavn27w3w2m03Lli274WDpN954Q0uWLNEPP/wg6erg61dffVWHDh1SxYoVJUlfffWVOnfurKNHjyokJEQDBgzQuXPntHz5cl24cEHlypXTunXr1Lx5c/t6n3rqKV28eDHX2gEUL/xWGoACV6tWLUVFRWnevHlq3bq1Dh48qE2bNunrr79WZmamXnvtNS1ZskRHjx5Venq60tPT5efn57COxo0b33I7n376qWbMmKEDBw4oNTVVGRkZKlmypMM8lSpVsociSWrevLmysrK0d+9ehYSEOMy7e/dupaWl2U/dZbt8+bL+9Kc/5fdlAOCCCEYA7opBgwZp6NChmj17tuLi4hQREaE2bdrojTfe0PTp0zVjxgzVr19ffn5+Gj58uH2Adbbrg9L1tm7dql69eumll15S+/btFRgYqI8++khTp0696XI2m83hv9fKysqSJK1atUphYWEObQz6Bv47EIwA3BU9evTQsGHDtGjRIi1YsECDBw+WzWbTpk2b9Oijj6pv376SroaR/fv3q3bt2vla/3fffaeIiAiNHz/ePi23K+GSkpJ07NgxhYaGSrp6ywA3NzfVqFEjx7x16tSRl5eXkpKS1KpVq3zVA6B4IBgBuCv8/f3Vs2dPjRs3TsnJyRowYIAkqVq1avrss8+0efNmlS5dWtOmTdOJEyfyHYyqVaumpKQkffTRR2rSpIlWrVqlZcuW5ZjP29tb/fv315tvvqmUlBT99a9/VY8ePXKcRpOuDuYeNWqURowYoaysLD3wwANKSUnR5s2b5e/vr/79+9/WawHAdXBVGoC7ZtCgQTp79qweeughVapUSZI0YcIENWrUSO3bt1d0dLRCQkJu687Tjz76qEaMGKGhQ4fqnnvu0ebNmzVhwoQc81WrVk3du3dXp06d1K5dO9WrV09vv/32Ddf7yiuv6MUXX9TkyZNVu3ZttW/fXp9//rkiIyPzXSMA18NVaQAAABZ6jAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALD8P1uPQGXwIVdEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Box plot of actual values for y1 and y2\n",
    "sns.boxplot(data=data[['Stage1.Output.Measurement0.U.Actual', 'Stage1.Output.Measurement0.U.Setpoint']])\n",
    "plt.xlabel('Variable')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Box Plot: Actual Values')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33209c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_combined = np.column_stack((y1_test, y2_test, y3_test, y4_test, y5_test))\n",
    "\n",
    "y_pred_combined = multioutput_regressor.predict(X_test)\n",
    "y_pred = np.hsplit(y_pred_combined, 5)\n",
    "\n",
    "plt.hist(y_test_combined[:, 0], bins=20, alpha=0.5, label='Actual')\n",
    "plt.hist(y_pred[0][:, 0], bins=20, alpha=0.5, label='Predicted')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution Comparison for y1')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6fbec9cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "77acf429",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)  # Choose the desired number of components\n",
    "X_train_pca = pca.fit_transform(X_train_scaled)\n",
    "X_test_pca = pca.transform(X_test_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bc7411e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 31.1576\n",
      "Epoch 2/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.1900\n",
      "Epoch 3/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.8194\n",
      "Epoch 4/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.6317\n",
      "Epoch 5/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4924\n",
      "Epoch 6/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3928\n",
      "Epoch 7/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3091\n",
      "Epoch 8/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2378\n",
      "Epoch 9/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1810\n",
      "Epoch 10/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1446\n",
      "Epoch 11/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1052\n",
      "Epoch 12/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0698\n",
      "Epoch 13/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0240\n",
      "Epoch 14/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0135\n",
      "Epoch 15/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9853\n",
      "Epoch 16/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9670\n",
      "Epoch 17/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9452\n",
      "Epoch 18/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9054\n",
      "Epoch 19/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.8793\n",
      "Epoch 20/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.8603\n",
      "Epoch 21/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.8443\n",
      "Epoch 22/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.8323\n",
      "Epoch 23/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.8229\n",
      "Epoch 24/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7884\n",
      "Epoch 25/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.8003\n",
      "Epoch 26/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7888\n",
      "Epoch 27/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7770\n",
      "Epoch 28/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7687\n",
      "Epoch 29/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7527\n",
      "Epoch 30/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7528\n",
      "Epoch 31/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7474\n",
      "Epoch 32/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7355\n",
      "Epoch 33/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7393\n",
      "Epoch 34/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7311\n",
      "Epoch 35/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7313\n",
      "Epoch 36/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7077\n",
      "Epoch 37/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7023\n",
      "Epoch 38/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.7001\n",
      "Epoch 39/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6991\n",
      "Epoch 40/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6833\n",
      "Epoch 41/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6764\n",
      "Epoch 42/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6798\n",
      "Epoch 43/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6704\n",
      "Epoch 44/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6594\n",
      "Epoch 45/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6565\n",
      "Epoch 46/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6399\n",
      "Epoch 47/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6510\n",
      "Epoch 48/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6450\n",
      "Epoch 49/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6316\n",
      "Epoch 50/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6263\n",
      "Epoch 51/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6257\n",
      "Epoch 52/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6182\n",
      "Epoch 53/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6157\n",
      "Epoch 54/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5977\n",
      "Epoch 55/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5962\n",
      "Epoch 56/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5871\n",
      "Epoch 57/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5748\n",
      "Epoch 58/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5746\n",
      "Epoch 59/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5630\n",
      "Epoch 60/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5596\n",
      "Epoch 61/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5549\n",
      "Epoch 62/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5372\n",
      "Epoch 63/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5297\n",
      "Epoch 64/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5293\n",
      "Epoch 65/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5340\n",
      "Epoch 66/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5291\n",
      "Epoch 67/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5038\n",
      "Epoch 68/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.5019\n",
      "Epoch 69/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4973\n",
      "Epoch 70/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4902\n",
      "Epoch 71/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4799\n",
      "Epoch 72/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4828\n",
      "Epoch 73/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4547\n",
      "Epoch 74/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4564\n",
      "Epoch 75/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4520\n",
      "Epoch 76/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4612\n",
      "Epoch 77/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4247\n",
      "Epoch 78/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4383\n",
      "Epoch 79/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4203\n",
      "Epoch 80/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4275\n",
      "Epoch 81/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4008\n",
      "Epoch 82/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4173\n",
      "Epoch 83/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4104\n",
      "Epoch 84/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4011\n",
      "Epoch 85/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.4082\n",
      "Epoch 86/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3852\n",
      "Epoch 87/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3987\n",
      "Epoch 88/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3909\n",
      "Epoch 89/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3757\n",
      "Epoch 90/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3916\n",
      "Epoch 91/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3540\n",
      "Epoch 92/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3766\n",
      "Epoch 93/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3845\n",
      "Epoch 94/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3511\n",
      "Epoch 95/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3601\n",
      "Epoch 96/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3561\n",
      "Epoch 97/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3630\n",
      "Epoch 98/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3562\n",
      "Epoch 99/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3335\n",
      "Epoch 100/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.3302\n",
      "80/80 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_shape=(X_train_pca.shape[1],)))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(5))  # 5 output neurons for the 5 target variables\n",
    "\n",
    "model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))\n",
    "model.fit(X_train_pca, y_train_combined, epochs=100, batch_size=32, verbose=1)\n",
    "\n",
    "y_pred_nn = model.predict(X_test_pca)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12e7a399",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Performance:\n",
      "MSE: 0.517007705651186\n",
      "RMSE: 0.719032478856961\n",
      "R-squared: 0.5523504335942336\n"
     ]
    }
   ],
   "source": [
    "mse_nn = mean_squared_error(y_test_combined, y_pred_nn)\n",
    "rmse_nn = np.sqrt(mse_nn)\n",
    "r2_nn = r2_score(y_test_combined, y_pred_nn)\n",
    "\n",
    "print(\"Neural Network Performance:\")\n",
    "print(\"MSE:\", mse_nn)\n",
    "print(\"RMSE:\", rmse_nn)\n",
    "print(\"R-squared:\", r2_nn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f68771e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3fd8118c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 36.2358\n",
      "Epoch 2/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 6.9877\n",
      "Epoch 3/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 5.8998\n",
      "Epoch 4/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 4.9960\n",
      "Epoch 5/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 4.5162\n",
      "Epoch 6/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 4.1118\n",
      "Epoch 7/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 3.6945\n",
      "Epoch 8/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 3.3898\n",
      "Epoch 9/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 3.1896\n",
      "Epoch 10/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.9448\n",
      "Epoch 11/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.7616\n",
      "Epoch 12/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.6185\n",
      "Epoch 13/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.5286\n",
      "Epoch 14/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.4284\n",
      "Epoch 15/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.4072\n",
      "Epoch 16/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.3592\n",
      "Epoch 17/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.2754\n",
      "Epoch 18/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.2481\n",
      "Epoch 19/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.2127\n",
      "Epoch 20/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.1972\n",
      "Epoch 21/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.1636\n",
      "Epoch 22/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.1497\n",
      "Epoch 23/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.0813\n",
      "Epoch 24/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.1487\n",
      "Epoch 25/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.0573\n",
      "Epoch 26/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.0649\n",
      "Epoch 27/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 2.0502\n",
      "Epoch 28/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.9902\n",
      "Epoch 29/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.9766\n",
      "Epoch 30/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.9453\n",
      "Epoch 31/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.8890\n",
      "Epoch 32/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.8931\n",
      "Epoch 33/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.8401\n",
      "Epoch 34/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.8192\n",
      "Epoch 35/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.7653\n",
      "Epoch 36/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.7461\n",
      "Epoch 37/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.7087\n",
      "Epoch 38/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.7166\n",
      "Epoch 39/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.6689\n",
      "Epoch 40/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.6501\n",
      "Epoch 41/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.6539\n",
      "Epoch 42/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.6068\n",
      "Epoch 43/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.6151\n",
      "Epoch 44/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.5723\n",
      "Epoch 45/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.5704\n",
      "Epoch 46/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.5468\n",
      "Epoch 47/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4654\n",
      "Epoch 48/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4858\n",
      "Epoch 49/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.5085\n",
      "Epoch 50/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4802\n",
      "Epoch 51/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4939\n",
      "Epoch 52/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4285\n",
      "Epoch 53/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4282\n",
      "Epoch 54/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4231\n",
      "Epoch 55/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4104\n",
      "Epoch 56/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3428\n",
      "Epoch 57/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.4115\n",
      "Epoch 58/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3334\n",
      "Epoch 59/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3391\n",
      "Epoch 60/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3484\n",
      "Epoch 61/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3080\n",
      "Epoch 62/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2859\n",
      "Epoch 63/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3290\n",
      "Epoch 64/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2768\n",
      "Epoch 65/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2693\n",
      "Epoch 66/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2594\n",
      "Epoch 67/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.3428\n",
      "Epoch 68/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2343\n",
      "Epoch 69/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2569\n",
      "Epoch 70/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2281\n",
      "Epoch 71/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2187\n",
      "Epoch 72/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2243\n",
      "Epoch 73/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2370\n",
      "Epoch 74/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1797\n",
      "Epoch 75/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.2581\n",
      "Epoch 76/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1505\n",
      "Epoch 77/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1587\n",
      "Epoch 78/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1696\n",
      "Epoch 79/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1327\n",
      "Epoch 80/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1551\n",
      "Epoch 81/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1688\n",
      "Epoch 82/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1145\n",
      "Epoch 83/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1264\n",
      "Epoch 84/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1069\n",
      "Epoch 85/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1250\n",
      "Epoch 86/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.1294\n",
      "Epoch 87/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0462\n",
      "Epoch 88/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0759\n",
      "Epoch 89/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0945\n",
      "Epoch 90/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0799\n",
      "Epoch 91/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0717\n",
      "Epoch 92/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0384\n",
      "Epoch 93/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0797\n",
      "Epoch 94/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0503\n",
      "Epoch 95/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0580\n",
      "Epoch 96/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0056\n",
      "Epoch 97/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0462\n",
      "Epoch 98/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0032\n",
      "Epoch 99/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0261\n",
      "Epoch 100/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 1.0001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1dd33f882e0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_shape=(X_train_pca.shape[1],)))\n",
    "model.add(Dropout(0.2))  # Add dropout regularization with a dropout rate of 0.2\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.2))  # Add dropout regularization with a dropout rate of 0.2\n",
    "model.add(Dense(5))\n",
    "\n",
    "model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))\n",
    "model.fit(X_train_pca, y_train_combined, epochs=100, batch_size=32, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e2969c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "361/361 [==============================] - 1s 3ms/step - loss: 0.9979 - val_loss: 0.8479\n",
      "Epoch 2/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9863 - val_loss: 0.8444\n",
      "Epoch 3/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9547 - val_loss: 0.8314\n",
      "Epoch 4/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9674 - val_loss: 0.8180\n",
      "Epoch 5/100\n",
      "361/361 [==============================] - 1s 2ms/step - loss: 0.9955 - val_loss: 0.7983\n",
      "Epoch 6/100\n",
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      "Epoch 81/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 1s 2ms/step - loss: 0.6941 - val_loss: 0.8165\n",
      "Epoch 82/100\n",
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      "Epoch 100/100\n",
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     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Train the model and obtain the history object\n",
    "history = model.fit(X_train_pca, y_train_combined, epochs=100, batch_size=32, validation_data=(X_test_pca, y_test_combined), verbose=1)\n",
    "\n",
    "# Extract loss values from the history object\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "# Plot the training and validation loss curves\n",
    "epochs = range(1, len(train_loss) + 1)\n",
    "\n",
    "plt.plot(epochs, train_loss, 'b', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d527281c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bc45e82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y_test_combined, y_pred_nn)\n",
    "rmse = np.sqrt(mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e850ac53",
   "metadata": {},
   "outputs": [],
   "source": [
    "r2 = r2_score(y_test_combined, y_pred_nn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eea8ea3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "MSE: 0.7355053292162477\n",
      "RMSE: 0.8576160733196689\n",
      "R-squared: 0.3656877025443027\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluation Metrics:\")\n",
    "print(\"MSE:\", mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "print(\"R-squared:\", r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "717e5b9a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}