2D Heat Transfer MLR model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(r\"C:\\Users\\Asus\\Desktop\\DDP\\Clean2d_master_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (s)</th>\n",
       "      <th>Material</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Part</th>\n",
       "      <th>Node</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_bottom</th>\n",
       "      <th>T_top</th>\n",
       "      <th>x/L</th>\n",
       "      <th>y/L</th>\n",
       "      <th>z/L</th>\n",
       "      <th>Tdiff_0.0005 (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>33</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time (s) Material     Alpha      Part  Node      X    Y  Z  Temperature  \\\n",
       "0      0.01   Copper  0.000111  PART-1-1     1  0.000  0.0  0        300.0   \n",
       "1      0.01   Copper  0.000111  PART-1-1     9  0.004  0.0  0        300.0   \n",
       "2      0.01   Copper  0.000111  PART-1-1    17  0.008  0.0  0        300.0   \n",
       "3      0.01   Copper  0.000111  PART-1-1    25  0.012  0.0  0        300.0   \n",
       "4      0.01   Copper  0.000111  PART-1-1    33  0.016  0.0  0        300.0   \n",
       "\n",
       "   T_left  T_right  T_bottom  T_top  x/L  y/L  z/L  Tdiff_0.0005 (s)  \n",
       "0     300      100       300    100  0.0  0.0    0              1.25  \n",
       "1     300      100       300    100  0.2  0.0    0              1.25  \n",
       "2     300      100       300    100  0.4  0.0    0              1.25  \n",
       "3     300      100       300    100  0.6  0.0    0              1.25  \n",
       "4     300      100       300    100  0.8  0.0    0              1.25  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (s)</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Node</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_bottom</th>\n",
       "      <th>T_top</th>\n",
       "      <th>x/L</th>\n",
       "      <th>y/L</th>\n",
       "      <th>z/L</th>\n",
       "      <th>Tdiff_0.0005 (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.0</td>\n",
       "      <td>3.801600e+04</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.000000</td>\n",
       "      <td>38016.00000</td>\n",
       "      <td>38016.00000</td>\n",
       "      <td>38016.0</td>\n",
       "      <td>19008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.289773</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.780572e+02</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.347419</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>280.420045</td>\n",
       "      <td>0.006831</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.546827e+02</td>\n",
       "      <td>111.804869</td>\n",
       "      <td>81.650732</td>\n",
       "      <td>50.000658</td>\n",
       "      <td>50.000658</td>\n",
       "      <td>0.34157</td>\n",
       "      <td>0.34157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.335009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.887560e-33</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.525520e+01</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.227250e+02</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>675.000000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.920000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time (s)         Alpha          Node             X             Y  \\\n",
       "count  38016.000000  38016.000000  38016.000000  38016.000000  38016.000000   \n",
       "mean       1.289773      0.000050    431.000000      0.010000      0.005000   \n",
       "std        3.347419      0.000041    280.420045      0.006831      0.003416   \n",
       "min        0.010000      0.000004      1.000000      0.000000      0.000000   \n",
       "25%        0.050000      0.000018    187.000000      0.004000      0.002000   \n",
       "50%        0.150000      0.000044    431.000000      0.010000      0.005000   \n",
       "75%        1.000000      0.000076    675.000000      0.016000      0.008000   \n",
       "max       20.000000      0.000111    861.000000      0.020000      0.010000   \n",
       "\n",
       "             Z   Temperature        T_left       T_right      T_bottom  \\\n",
       "count  38016.0  3.801600e+04  38016.000000  38016.000000  38016.000000   \n",
       "mean       0.0  1.780572e+02    450.000000    100.000000    350.000000   \n",
       "std        0.0  1.546827e+02    111.804869     81.650732     50.000658   \n",
       "min        0.0 -2.887560e-33    300.000000      0.000000    300.000000   \n",
       "25%        0.0  4.525520e+01    375.000000      0.000000    300.000000   \n",
       "50%        0.0  1.227250e+02    450.000000    100.000000    350.000000   \n",
       "75%        0.0  3.000000e+02    525.000000    200.000000    400.000000   \n",
       "max        0.0  6.000000e+02    600.000000    200.000000    400.000000   \n",
       "\n",
       "              T_top          x/L          y/L      z/L  Tdiff_0.0005 (s)  \n",
       "count  38016.000000  38016.00000  38016.00000  38016.0      19008.000000  \n",
       "mean      50.000000      0.50000      0.50000      0.0          1.585000  \n",
       "std       50.000658      0.34157      0.34157      0.0          0.335009  \n",
       "min        0.000000      0.00000      0.00000      0.0          1.250000  \n",
       "25%        0.000000      0.20000      0.20000      0.0          1.250000  \n",
       "50%       50.000000      0.50000      0.50000      0.0          1.585000  \n",
       "75%      100.000000      0.80000      0.80000      0.0          1.920000  \n",
       "max      100.000000      1.00000      1.00000      0.0          1.920000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((38016, 8), (38016,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "#storing starting time\n",
    "begin = time.time()\n",
    "\n",
    "#seperating independent and dependent variables, Temperature is the only dependent variable in our case\n",
    "x = data[['T_left', 'T_right','T_bottom', 'T_top', 'Time (s)', 'x/L', 'y/L','Alpha']]\n",
    "y = data['Temperature']\n",
    "x.shape, y.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating Training-Testing data from the new data frame\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_x,test_x,train_y,test_y = train_test_split(x, y , test_size = 0.25, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Linear Regression and metric mean absolute error\n",
    "\n",
    "from sklearn.linear_model import LinearRegression as LR\n",
    "from sklearn.metrics import mean_absolute_error as mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "303.6833755519048\n"
     ]
    }
   ],
   "source": [
    "lr = LR()\n",
    "# Fitting the model\n",
    "lr.fit(train_x, train_y)\n",
    "\n",
    "print(lr.intercept_) #Intercept of the Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime of Linear Regression program (in seconds) is 2.2309844493865967\n"
     ]
    }
   ],
   "source": [
    "#storing end time\n",
    "time.sleep(1)\n",
    "end = time.time()\n",
    "\n",
    "#Calculating runtime\n",
    "print (\"Total runtime of Linear Regression program (in seconds) is\", end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 15))\n",
    "sns.heatmap(train_x.corr(), annot = True, cmap=\"RdYlGn\", vmin = -1, vmax = 1)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error 85.53069685939165\n"
     ]
    }
   ],
   "source": [
    "# Predicting over the Train Set and calculating error\n",
    "train_predict = lr.predict(train_x)\n",
    "k = mae(train_predict, train_y)\n",
    "print('Training Mean Absolute Error', k )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Absolute Error     83.48081914188836\n"
     ]
    }
   ],
   "source": [
    "# Predicting over the Test Set and calculating error\n",
    "test_predict = lr.predict(test_x)\n",
    "k = mae(test_predict, test_y)\n",
    "print('Test Mean Absolute Error    ', k )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Variables    Coefficient\n",
      "0    T_left       0.137578\n",
      "1   T_right       0.157611\n",
      "2  T_bottom      -0.013467\n",
      "3     T_top       0.013467\n",
      "4  Time (s)       4.675102\n",
      "5       x/L    -215.174172\n",
      "6       y/L    -223.036744\n",
      "7     Alpha  275372.449570\n"
     ]
    }
   ],
   "source": [
    "#Regression Coefficients of the variables in order \n",
    "Coeff_data = {'Variables': ['T_left', 'T_right', 'T_bottom', 'T_top', 'Time (s)', 'x/L', 'y/L', 'Alpha'], 'Coefficient': lr.coef_}\n",
    "df = pd.DataFrame(Coeff_data, columns = ['Variables', 'Coefficient'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficient plot')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Unscaled Coefficient vs Variable plot\n",
    "plt.figure(figsize=(8, 6), dpi=120, facecolor='w', edgecolor='b')\n",
    "x = range(len(train_x.columns))\n",
    "y = lr.coef_\n",
    "plt.bar( x, y )\n",
    "plt.xlabel( \"Variables\")\n",
    "plt.ylabel('Coefficients')\n",
    "plt.title('Coefficient plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.269012785796136"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_x, train_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.89495589103898"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_x,test_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[154.1923479  144.72078927 142.60311272 ... 194.75633582 -42.4823442\n",
      " 123.01551111]\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(test_x)\n",
    "print (y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5771</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>154.192348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27401</th>\n",
       "      <td>-8.259910e-35</td>\n",
       "      <td>144.720789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22628</th>\n",
       "      <td>2.732510e+01</td>\n",
       "      <td>142.603113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12451</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>166.985024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13111</th>\n",
       "      <td>8.743740e+01</td>\n",
       "      <td>286.096164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35221</th>\n",
       "      <td>2.681660e+02</td>\n",
       "      <td>333.322171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13384</th>\n",
       "      <td>9.986740e+01</td>\n",
       "      <td>25.413664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9154</th>\n",
       "      <td>1.324940e+02</td>\n",
       "      <td>194.756336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16235</th>\n",
       "      <td>-8.480660e-42</td>\n",
       "      <td>-42.482344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6065</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>123.015511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9504 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Actual   Predicted\n",
       "5771   1.000000e+02  154.192348\n",
       "27401 -8.259910e-35  144.720789\n",
       "22628  2.732510e+01  142.603113\n",
       "12451  1.000000e+02  166.985024\n",
       "13111  8.743740e+01  286.096164\n",
       "...             ...         ...\n",
       "35221  2.681660e+02  333.322171\n",
       "13384  9.986740e+01   25.413664\n",
       "9154   1.324940e+02  194.756336\n",
       "16235 -8.480660e-42  -42.482344\n",
       "6065   1.000000e+02  123.015511\n",
       "\n",
       "[9504 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':test_y, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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07DXXXMOTTz7Zabn23Hnnnbzvfe9bZ9zf//53hgwZwvLl6/7nvOOPP54bb7yxS3VZdQ54szp1/fXXc9hhhzFt2rROy25owFez7bbbMmHCBG666aY145YvX87dd9/tAO8hDnizOvTyyy9zzz33cNVVV60X8Jdeein7778/BxxwAOeddx7Tp0+nubmZU045hTFjxvDqq6/S0NDAc88V/3u7ubmZI444AoD77ruPQw45hLFjx3LIIYewYMGCDttx8sknr7P8GTNmMHHiRLbZZpua6rrwwgu57LK1/1Rsv/32Y9GiRQD85Cc/Yfz48YwZM4ZPfepTrFq1of+pc8vjyyTNetPnPgdz5vRsnWPGwHe+02GRm266iYkTJ7L33nszePBgHnjgAcaNG8dvfvMbbrrpJmbNmsU222zDsmXLGDx4MN///ve57LLLaGxs7LDeffbZh7vuuou+ffty6623csEFF/Dzn/+83fITJ07kE5/4BEuXLmXIkCFMmzaNz3zmM92qq2z+/PnccMMN3HPPPfTr148zzzyTqVOncuqpp9Y0fy4c8GZ16Prrr+dzn/scAE1NTVx//fWMGzeOW2+9lY997GNss03xP8QHDx7cpXqXL1/O5MmTeeSRR5DEG290+B/l6N+/P8cddxzTp0/nhBNOYM6cOUyYMKFbdZXddtttzJ49m4MOOgiAV199lV122aVL65IDB7xZb+rkSHtjWLp0KbfffjsPP/wwkli1ahWSuPTSS4mImi4Z7Nu3L6tXrwZY5/rxr371qxx55JHMmDGDRYsWrem66cjJJ5/MxRdfTEQwadKkNb8mraWucjvKbYkIJk+ezH/91391uvycuQ/erM5Mnz6dU089lccff5xFixaxePFiRowYwd13382ECROYMmUKr7zyCgDLli0DYLvttuOll15aU0dDQwOzZ88GWKfbZPny5ey+++5AcWK2FkceeSSPPPIIV1xxBSeffHKX6mpoaOCBBx4A4IEHHmDhwoUAHH300UyfPp1nn312zXo8/vgWfeffbqnLgH/6aXj00d5uhVnvuP766/nABz6wzrgTTjiBn/70p0ycOJHjjjuOxsZGxowZs+YE5kc/+lHOOOOMNSdZv/a1r3HOOefwj//4j/Tp02dNPf/6r//K+eefz6GHHlrzSc2tttqKE044gaVLl3L44Yd3qa4TTjiBZcuWMWbMGH74wx+y9957AzBq1CguvvhiJkyYwNvf/naOOeYYnnpqi/v/2hssi//J2tjYGF35hx9t30AzWHXbAs2fP5999923t5thFaq9LpJmR0THZ5Y3YzUdwUuaKGmBpBZJ51WZPkDSDWn6LEkNpWnnp/ELJB2bxg2UdJ+kByXNlfTvpfLXSFooaU56jNnw1TQzqz+dnmSV1Ae4AjgGaAXulzQzIuaVip0GPB8Re0lqAi4BTpI0CmgCRgO7AbdK2htYARwVES9L6gfcLek3EfGnVN+XImJ6T62kmVk9quUIfjzQEhGPRcTrwDRgUkWZScC16fl04GgVp+InAdMiYkVELARagPFReDmV75ce7jAxM+tBtQT87sDi0nBrGle1TESsBJYDQzqaV1IfSXOAZ4HfR8SsUrn/kPSQpMslDajWKEmnS2qW1LxkyZIaVsPMrL7UEvDVLoqtPNpur0y780bEqogYAwwHxkvaL00/H9gHOAgYDJxbrVERcWVENEZE49ChQztfCzOzOlNLwLcCe5SGhwOVdx1aU0ZSX2AHYFkt80bEC8CdwMQ0/FTqwlkBXE3RRWRmZl1US8DfD4yUNEJSf4qTpjMryswEJqfnJwK3R3H95UygKV1lMwIYCdwnaaikHQEkbQ28C/hrGh6W/go4Hnh4Q1bQzNbXp08fxowZs+axaNEimpub+exnPwsUt+S9995715S/6aabmDdvXnvVtWvQoEHrjTviiCO45ZZb1hn3ne98hzPPPLPLdVnHOr2KJiJWSjobuAXoA0yJiLmSLgKaI2ImcBVwnaQWiiP3pjTvXEk3AvOAlcBZEbEqhfi16QqdrYAbI+KXaZFTJQ2l6N6ZA5zRkytsZrD11lszp+ImZw0NDWtuJnbnnXcyaNAgDjnkEKAI+Pe9732MGjVqg5fddgfJY489ds24adOm8c1vfnOD67YKEbHFPw488MDoiuInTl2axazHzJs3r7ebENtuu+164+64445473vfGwsXLoxdd901dttttzjggAPizjvvjJ122ikaGhrigAMOiJaWlmhpaYljjz02xo0bF4cddljMnz8/IiIee+yxOPjgg6OxsTG+8pWvVF3Oc889FzvvvHO89tprERGxcOHC2GOPPWL16tXx0ksvxVFHHRVjx46N/fbbL2666ab12tzWzjZnnXVWXH311RER0dzcHIcffniMGzcuJkyYEE8++WRERHz3u9+NfffdN/bff/846aSTqm6Taq8LxUFsr2dcdx++2ZhZL+qluwXz6quvMmZM8RvCESNGMGPGjDXTGhoaOOOMMxg0aBBf/OIXATjuuON43/vex4knnggU93r50Y9+xMiRI5k1axZnnnkmt99+O+eccw6f/vSnOfXUU7niiiuqLnvIkCGMHz+e3/72t0yaNIlp06Zx0kknIYmBAwcyY8YMtt9+e5577jkOPvhgjjvuuJpugPbGG2/wmc98hptvvpmhQ4dyww038OUvf5kpU6bwjW98g4ULFzJgwABeeOGFWjZjFhzwZnWoWhdNrV5++WXuvfdePvjBD64Zt2LFCgDuueeeNTcf+8hHPsK551a9CG5NN01bwE+ZMgUoehQuuOAC7rrrLrbaaiueeOIJnnnmGd70pjd12q4FCxbw8MMPc8wxxwCwatUqhg0bBsDb3/52TjnlFI4//niOP/74bq33lsgBb9aLeuFuwRts9erV7Ljjju1+QNRytH388cfzhS98gQceeIBXX32VcePGATB16lSWLFnC7Nmz6devHw0NDevcjhg6vkXw6NGj+eMf/7je8n71q19x1113MXPmTL7+9a8zd+5c+vbNP/7q8m6SZtaxytsDl4e33357RowYwc9+9jOgCNYHH3wQgEMPPXTNv+CbOnVqu/UPGjSII444go9//OPr3SJ4l112oV+/ftxxxx1Vb/G75557Mm/ePFasWMHy5cu57bbbAHjb297GkiVL1gT8G2+8wdy5c1m9ejWLFy/myCOP5NJLL+WFF17g5ZdfXq/eHDngzWw973//+5kxYwZjxozhD3/4A01NTXzzm99k7NixPProo0ydOpWrrrqKAw44gNGjR3PzzTcD8N3vfpcrrriCgw46iOXLl3e4jJNPPpkHH3yQpqamNeNOOeUUmpubaWxsZOrUqeyzzz7rzbfHHnvwoQ99aE23y9ixY4Hiv0NNnz6dc889lwMOOIAxY8Zw7733smrVKj784Q+z//77M3bsWD7/+c+z44479uDW2nz5dsFmm5hvF7x5qtvbBZuZ2ZbHAW9mlikHvFkvyKFrNCe5vh4OeLNNbODAgSxdujTbUNnSRARLly5l4MCBvd2UHpf/haBmm5nhw4fT2tqK/4/B5mPgwIEMHz68t5vR4xzwZptYv379GDFiRG83w+qAu2jMzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsUw54M7NMOeDNzDLlgDczy1RNAS9poqQFkloknVdl+gBJN6TpsyQ1lKadn8YvkHRsGjdQ0n2SHpQ0V9K/l8qPSHU8kursv+GraWZWfzoNeEl9gCuAdwOjgJMljaoodhrwfETsBVwOXJLmHQU0AaOBicAPUn0rgKMi4gBgDDBR0sGprkuAyyNiJPB8qtvMzLqoliP48UBLRDwWEa8D04BJFWUmAdem59OBoyUpjZ8WESsiYiHQAoyPwsupfL/0iDTPUakOUp3Hd3PdzMzqWi0BvzuwuDTcmsZVLRMRK4HlwJCO5pXUR9Ic4Fng9xExK83zQqqjvWWR5j9dUrOk5iVLltSwGmZm9aWWgFeVcVFjmXbnjYhVETEGGA6Ml7RfjcsizX9lRDRGROPQoUPbbbyZWb2qJeBbgT1Kw8OBJ9srI6kvsAOwrJZ5I+IF4E6KPvrngB1THe0ty8zMalBLwN8PjExXt/SnOGk6s6LMTGByen4icHtERBrflK6yGQGMBO6TNFTSjgCStgbeBfw1zXNHqoNU583dXz0zs/rVt7MCEbFS0tnALUAfYEpEzJV0EdAcETOBq4DrJLVQHLk3pXnnSroRmAesBM6KiFWShgHXpitqtgJujIhfpkWeC0yTdDHw51S3mZl1kYqD5i1bY2NjNDc311xeqac/g1U3s41I0uyIaOztdnSXf8lqZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmXLAm5llygFvZpYpB7yZWaYc8GZmmaop4CVNlLRAUouk86pMHyDphjR9lqSG0rTz0/gFko5N4/aQdIek+ZLmSjqnVP5CSU9ImpMe79nw1TQzqz99OysgqQ9wBXAM0ArcL2lmRMwrFTsNeD4i9pLUBFwCnCRpFNAEjAZ2A26VtDewEviXiHhA0nbAbEm/L9V5eURc1lMraWZWj2o5gh8PtETEYxHxOjANmFRRZhJwbXo+HThaktL4aRGxIiIWAi3A+Ih4KiIeAIiIl4D5wO4bvjpmZtamloDfHVhcGm5l/TBeUyYiVgLLgSG1zJu6c8YCs0qjz5b0kKQpknaq1ihJp0tqltS8ZMmSGlbDzKy+1BLwqjIuaizT4bySBgE/Bz4XES+m0T8E3gqMAZ4CvlWtURFxZUQ0RkTj0KFDO14DM7M6VEvAtwJ7lIaHA0+2V0ZSX2AHYFlH80rqRxHuUyPiF20FIuKZiFgVEauB/6boIjIzsy6qJeDvB0ZKGiGpP8VJ05kVZWYCk9PzE4HbIyLS+KZ0lc0IYCRwX+qfvwqYHxHfLlckaVhp8APAw11dKTMzq+EqmohYKels4BagDzAlIuZKughojoiZFGF9naQWiiP3pjTvXEk3AvMorpw5KyJWSToM+AjwF0lz0qIuiIhfA5dKGkPRlbMI+FQPrq+ZWd1QcaC9ZWtsbIzm5uaayyudGchg1c1sI5I0OyIae7sd3eVfspqZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWKQe8mVmmHPBmZplywJuZZcoBb2aWqZoCXtJESQsktUg6r8r0AZJuSNNnSWooTTs/jV8g6dg0bg9Jd0iaL2mupHNK5QdL+r2kR9LfnTZ8Nc3M6k+nAS+pD3AF8G5gFHCypFEVxU4Dno+IvYDLgUvSvKOAJmA0MBH4QapvJfAvEbEvcDBwVqnO84DbImIkcFsaNjOzLqrlCH480BIRj0XE68A0YFJFmUnAten5dOBoSUrjp0XEiohYCLQA4yPiqYh4ACAiXgLmA7tXqeta4PjurZqZWX2rJeB3BxaXhltZG8brlYmIlcByYEgt86bunLHArDRq14h4KtX1FLBLtUZJOl1Ss6TmJUuW1LAaZmb1pZaAV5VxUWOZDueVNKKFKVkAABKoSURBVAj4OfC5iHixhrasrSTiyohojIjGoUOHdmVWM7O6UEvAtwJ7lIaHA0+2V0ZSX2AHYFlH80rqRxHuUyPiF6Uyz0galsoMA56tdWXMzGytWgL+fmCkpBGS+lOcNJ1ZUWYmMDk9PxG4PSIijW9KV9mMAEYC96X++auA+RHx7Q7qmgzc3NWVMjMz6NtZgYhYKels4BagDzAlIuZKughojoiZFGF9naQWiiP3pjTvXEk3AvMorpw5KyJWSToM+AjwF0lz0qIuiIhfA98AbpR0GvA34IM9ucJmZvVCxYH2lq2xsTGam5trLq90ZiCDVTezjUjS7Iho7O12dJd/yWpmlikHvJlZphzwZmaZcsCbmWXKAW9mlikHvJlZphzwZmaZcsCbmWXKAW9mlikHvJlZphzwZmaZcsCbmWXKAW9mlikHvJlZphzwZmaZcsCbmWXKAW9mlikHvJlZphzwZmaZcsCbmWXKAW9mlikHvJlZphzwZmaZcsCbmWXKAW9mlqm6DviI3m6BmdnGU9cBb2aWMwe8mVmmHPBmZplywJuZZaquA94nWc0sZ3Ud8GZmOXPAm5llygFvZpapug5498GbWc7qOuDNzHLmgDczy5QD3swsU3Ud8O6DN7Oc1XXAm5nlzAFvZpYpB7yZWaZqCnhJEyUtkNQi6bwq0wdIuiFNnyWpoTTt/DR+gaRjS+OnSHpW0sMVdV0o6QlJc9LjPd1fPTOz+tVpwEvqA1wBvBsYBZwsaVRFsdOA5yNiL+By4JI07yigCRgNTAR+kOoDuCaNq+byiBiTHr/u2irVzidZzSxntRzBjwdaIuKxiHgdmAZMqigzCbg2PZ8OHC1Jafy0iFgREQuBllQfEXEXsKwH1sHMzKqoJeB3BxaXhlvTuKplImIlsBwYUuO81Zwt6aHUjbNTtQKSTpfULKl5yZIlNVRpZlZfagl4VRlX2bnRXpla5q30Q+CtwBjgKeBb1QpFxJUR0RgRjUOHDu2kSjOz+lNLwLcCe5SGhwNPtldGUl9gB4rul1rmXUdEPBMRqyJiNfDfpC6djcF98GaWs1oC/n5gpKQRkvpTnDSdWVFmJjA5PT8RuD0iIo1vSlfZjABGAvd1tDBJw0qDHwAebq+smZm1r29nBSJipaSzgVuAPsCUiJgr6SKgOSJmAlcB10lqoThyb0rzzpV0IzAPWAmcFRGrACRdDxwB7CypFfhaRFwFXCppDEVXziLgUz25wmZm9UKRQT9FY2NjNDc311xe6czAihXQv/9GapSZbfEkzY6Ixt5uR3fV9S9ZM/hsMzNrV10HvJlZzhzwZmaZcsCbmWXKAW9mlqm6DnifZDWznNV1wJuZ5cwBb2aWKQe8mVmm6jrg3QdvZjmr64A3M8uZA97MLFMOeDOzTNV1wLsP3sxyVtcBb2aWMwe8mVmmHPBmZplywJuZZaquA94nWc0sZ3Ud8GZmOXPAm5llygFvZpapug5498GbWc7qOuDNzHLmgDczy5QD3swsU3Ud8O6DN7Oc1XXAm5nlzAFvZpYpB7yZWaYc8GZmmarrgPdJVjPLWV0HvJlZzhzwZmaZcsCbmWWqrgPeffBmlrO6Dngzs5w54M3MMuWANzPLVF0HvPvgzSxndR3wZmY5c8CbmWWqpoCXNFHSAkktks6rMn2ApBvS9FmSGkrTzk/jF0g6tjR+iqRnJT1cUddgSb+X9Ej6u1P3V8/MrH51GvCS+gBXAO8GRgEnSxpVUew04PmI2Au4HLgkzTsKaAJGAxOBH6T6AK5J4yqdB9wWESOB29KwmZl1US1H8OOBloh4LCJeB6YBkyrKTAKuTc+nA0dLUho/LSJWRMRCoCXVR0TcBSyrsrxyXdcCx3dhfbrEJ1nNLGe1BPzuwOLScGsaV7VMRKwElgNDapy30q4R8VSq6ylgl2qFJJ0uqVlS85IlS2pYDTOz+lJLwKvKuMpj3/bK1DJvt0TElRHRGBGNQ4cO7YkqzcyyUkvAtwJ7lIaHA0+2V0ZSX2AHiu6XWuat9IykYamuYcCzNbTRzMwq1BLw9wMjJY2Q1J/ipOnMijIzgcnp+YnA7RERaXxTuspmBDASuK+T5ZXrmgzcXEMbu8V98GaWs04DPvWpnw3cAswHboyIuZIuknRcKnYVMERSC/AF0pUvETEXuBGYB/wWOCsiVgFIuh74I/A2Sa2STkt1fQM4RtIjwDFp2MzMukiRwWFsY2NjNDc311xe6czA0qUwePBGapSZbfEkzY6Ixt5uR3f5l6xmZpmq64DP4MuLmVm76jrgzcxy5oA3M8uUA97MLFN1HfDugzeznNV1wJuZ5cwBb2aWKQe8mVmmHPBmZpmq64D3SVYzy1ldB7yZWc4c8GZmmXLAm5llqq4D3n3wZpazug54M7OcOeDNzDLlgDczy1RdB7z74M0sZ3Ud8GZmOXPAm5llygFvZpYpB7yZWabqOuB9ktXMclbXAW9mljMHvJlZphzwZmaZquuAdx+8meWsrgPezCxnDngzs0w54M3MMlXXAe8+eDPLWV0HvJlZzhzwZmaZcsCbmWXKAW9mlqm6DnifZDWznNV1wJuZ5cwBb2aWKQe8mVmm6jrg3QdvZjmr64A3M8tZTQEvaaKkBZJaJJ1XZfoASTek6bMkNZSmnZ/GL5B0bGd1SrpG0kJJc9JjzIatoplZferbWQFJfYArgGOAVuB+STMjYl6p2GnA8xGxl6Qm4BLgJEmjgCZgNLAbcKukvdM8HdX5pYiY3gPrZ2ZWt2o5gh8PtETEYxHxOjANmFRRZhJwbXo+HThaktL4aRGxIiIWAi2pvlrq3OjcB29mOasl4HcHFpeGW9O4qmUiYiWwHBjSwbyd1fkfkh6SdLmkATW00czMKtQS8KoyrvLYt70yXR0PcD6wD3AQMBg4t2qjpNMlNUtqXrJkSbUiZmZ1rZaAbwX2KA0PB55sr4ykvsAOwLIO5m23zoh4KgorgKspunPWExFXRkRjRDQOHTq0htUwM6svtQT8/cBISSMk9ac4aTqzosxMYHJ6fiJwe0REGt+UrrIZAYwE7uuoTknD0l8BxwMPb8gKmpnVq06voomIlZLOBm4B+gBTImKupIuA5oiYCVwFXCepheLIvSnNO1fSjcA8YCVwVkSsAqhWZ1rkVElDKbpx5gBn9NzqVq7bxqrZzKz3KTJIucbGxmhubq65vNIZgMcfhze/eSM1ysy2eJJmR0Rjb7eju/xLVjOzTDngzcwyVdcBn0HvlJlZu+o64M3McuaANzPLlAPezCxTdR3w7oM3s5zVdcCbmeXMAW9mlikHvJlZphzwZmaZquuA90lWM8tZXQe8mVnOHPBmZplywJuZZaquA9598GaWs7oOeDOznDngzcwy5YA3M8tUXQe8++DNLGd1HfBmZjlzwJuZZcoBb2aWKQe8mVmm6jrgfZLVzHJW1wFvZpYzB7yZWaYc8GZmmarrgHcfvJnlrK4D3swsZw54M7NMOeDNzDJV1wHvPngzy1ldB7yZWc4c8GZmmXLAm1n+HnsM7r4b3nijt1uySTngzSx/114L73wnrFjR2y3ZpOo64H2S1axOzJoFo0fDoEG93ZJNqq4D3szqQATcdx+84x293ZJNzgFvZnl75BF4/nkHvJlZdmbNKv464OuL++DN6sCsWUXf+6hRvd2STa6uA97M6sB990FjI/Tp09st2eRqCnhJEyUtkNQi6bwq0wdIuiFNnyWpoTTt/DR+gaRjO6tT0ohUxyOpzv4btooduPJKaGnZaNWbWS955BG4+mr4+99hzpy67J4B6NtZAUl9gCuAY4BW4H5JMyNiXqnYacDzEbGXpCbgEuAkSaOAJmA0sBtwq6S90zzt1XkJcHlETJP0o1T3D3tiZddz+bfh6nOLa2RfeQVuvhnGj4dTToFnnil2jLe8BQ48sDhJs2gR7Lor7LlnseM8/TQMHgxDhsDrrxdlttsOttkGVq2Cl18unvfvD6tXw6uvwsCBxZFERDFP//4gFe1ZtQq22mrt8OrVxbDZ5mb16uJv2/4ZUYxrO0pevbr4UVHb/r1yZbG/b711Mfzaa8Vju+2KOl56qXgPDh4MffvCc8/Biy/CsGEwYAAsXAjLlsFb31q8p5qbobW1ODIfNAiuuw4WLIB/+qfivfnJTxZ1futbRTsc8O0aD7RExGMAkqYBk4BywE8CLkzPpwPfl6Q0flpErAAWSmpJ9VGtTknzgaOAf05lrk319lzAz51L8XkDr1z0Lfj5l2HSpGLa4MEwbRp84Qsd17HVVmt3cCh2yJUr1w7367fuL+b69y927jYDBxbDbXVsvXUx3BbwAwYU869cWbxhBgwoyq1evf5DKuapfLS94cp/I4rybfOU/7ZNL5dv0/aB0zZvteewdp72/pZVzlv5t0153mr1dXQipVxXtforl7Wpl9e2vSuX1d72qratytu+/Dq37VttZcqvtbR2/2kr2/a8vF+0PaRi31y1qii3atXadvXrV+z/r71WzN+379phKObv12/tD4ykYrj8fqh8v1S+nyrfb237a6Xtty+O2qEI9KYmOP/8tcN1qJaA3x1YXBpuBSq31poyEbFS0nJgSBr/p4p5d0/Pq9U5BHghIlZWKb8OSacDp6fBlyUtqGFdynY+8N94bp0xy5bVNmd5Z4N1d0ZY/+fQ5Z0Z1u78bV59dd26y8OrVhVHNmvtDKV2R6x9823e1m33lqO+2t3V/eiNN9bd31euXPf9sHr1ur8ebfvmWlnHWjuzcuW67a58v7X3Ifvii2ufz5q19uoZgN2rxkgt9uzujJuDWgK+ymEOlVu4vTLtja/W79BR+fVHRlwJXFltWi0kNUdEY3fn7y1u96bldm9aW2q7N1e1dPC2AnuUhocDT7ZXRlJfYAdgWQfztjf+OWDHVEd7yzIzsxrUEvD3AyPT1S39KU6azqwoMxOYnJ6fCNweEZHGN6WrbEYAI4H72qszzXNHqoNU583dXz0zs/rVaRdN6lM/G7gF6ANMiYi5ki4CmiNiJnAVcF06ibqMIrBJ5W6kOCG7EjgrIlYBVKszLfJcYJqki4E/p7o3hm537/Qyt3vTcrs3rS213ZslhX/OaWaWJV9kbWaWKQe8mVmm6i7gO7vtwiZsxyJJf5E0R1JzGjdY0u/TbRp+L2mnNF6Svpfa/JCkcaV6Jqfyj0iaXBp/YKq/Jc1b7RLUWto5RdKzkh4ujdvo7WxvGRvY7gslPZG2+RxJ7ylN65FbaqiD23bU2O49JN0hab6kuZLO6Wh7bC7bvIN2b/bbPGsRUTcPihO6jwJvAfoDDwKjeqkti4CdK8ZdCpyXnp8HXJKevwf4DcXvBA4GZqXxg4HH0t+d0vOd0rT7gH9I8/wGeHc323k4MA54eFO2s71lbGC7LwS+WKXsqLQvDABGpH2kT0f7C3Aj0JSe/wj4dHp+JvCj9LwJuKGL7R4GjEvPtwP+N7Vvs97mHbR7s9/mOT96vQGbdGWLnfqW0vD5wPm91JZFrB/wC4Bh6fkwYEF6/mPg5MpywMnAj0vjf5zGDQP+Whq/TrlutLWBdYNyo7ezvWVsYLvbC5t19gOKq7v+ob39JQXjc0Dfyv2qbd70vG8qpw3Y9jdT3LNpi9jmVdq9xW3znB711kVT7bYL3f4N8wYK4HeSZqu47QLArhHxFED6u0sa3167OxrfWmV8T9kU7WxvGRvq7NSVMaXUBdHVdnd0S411btsBtN22o8tSV8NYYBZb0DavaDdsQds8N/UW8DXfCmETODQixgHvBs6SdHgHZbt6K4jeWs/NvZ0/BN4KjAGeAr6Vxvdku3tknSQNAn4OfC4iXuyoaDvL65VtXqXdW8w2z1G9BXwtt13YJCLiyfT3WWAGxV02n5E0DCD9fTYV7+otH1rT88rxPWVTtLO9ZXRbRDwTEasiYjXw36y9s2lP3lKjvdt21ExSP4qQnBoRv0ijN/ttXq3dW8o2z1W9BXwtt13Y6CRtK2m7tufABOBh1r3lQ/k2DTOBU9MVEwcDy9NX6FuACZJ2Sl99J1D0Sz4FvCTp4HSFxKn07C0fNkU721tGt7WFV/IBim3etqyeuqVGe7ftqLWNovj19vyI+HZp0ma9zdtr95awzbPW2ycBNvWD4qqD/6U4U//lXmrDWyiuDngQmNvWDop+w9uAR9LfwWm8KP5ByqPAX4DGUl0fB1rS42Ol8Y0Ub6ZHge/TzZNOwPUUX63foDhSOm1TtLO9ZWxgu69L7XqIIhSGlcp/ObVhAaUrjtrbX9JreF9an58BA9L4gWm4JU1/SxfbfRhF98JDwJz0eM/mvs07aPdmv81zfvhWBWZmmaq3Lhozs7rhgDczy5QD3swsUw54M7NMOeDNzDLlgDczy5QD3swsU/8/DniRBWHX0tgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(5, 7))\n",
    "\n",
    "\n",
    "ax = sns.distplot(y, hist=False, color=\"r\", label=\"Actual Value\")\n",
    "sns.distplot(y_pred, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax)\n",
    "\n",
    "\n",
    "plt.title('Actual vs Fitted Values for Temperature (Linear Regression)')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling the Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Time (s)</th>\n",
       "      <th>Material</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Part</th>\n",
       "      <th>Node</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>T_left</th>\n",
       "      <th>...</th>\n",
       "      <th>z/L</th>\n",
       "      <th>Tdiff_0.0005 (s)</th>\n",
       "      <th>scaled_T_left</th>\n",
       "      <th>scaled_T_right</th>\n",
       "      <th>scaled_T_bottom</th>\n",
       "      <th>scaled_T_top</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>scaled_location_x</th>\n",
       "      <th>scaled_location_y</th>\n",
       "      <th>scaled_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.382321</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>1.469745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.382321</td>\n",
       "      <td>-0.87831</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>1.469745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.382321</td>\n",
       "      <td>-0.29277</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>1.469745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.382321</td>\n",
       "      <td>0.29277</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>1.469745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>33</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.382321</td>\n",
       "      <td>0.87831</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>1.469745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38011</th>\n",
       "      <td>20.00</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>829</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>3.127410e-34</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>-1.224745</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.589522</td>\n",
       "      <td>-0.87831</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>-1.135726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38012</th>\n",
       "      <td>20.00</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>837</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1.775720e-34</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>-1.224745</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.589522</td>\n",
       "      <td>-0.29277</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>-1.135726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38013</th>\n",
       "      <td>20.00</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>845</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1.301770e-34</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>-1.224745</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.589522</td>\n",
       "      <td>0.29277</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>-1.135726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38014</th>\n",
       "      <td>20.00</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>853</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>7.662100e-35</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>-1.224745</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.589522</td>\n",
       "      <td>0.87831</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>-1.135726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38015</th>\n",
       "      <td>20.00</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>861</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.605190e-44</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>-1.224745</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.589522</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>-1.135726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38016 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time (s)   Material     Alpha      Part  Node      X     Y  Z  \\\n",
       "0          0.01     Copper  0.000111  PART-1-1     1  0.000  0.00  0   \n",
       "1          0.01     Copper  0.000111  PART-1-1     9  0.004  0.00  0   \n",
       "2          0.01     Copper  0.000111  PART-1-1    17  0.008  0.00  0   \n",
       "3          0.01     Copper  0.000111  PART-1-1    25  0.012  0.00  0   \n",
       "4          0.01     Copper  0.000111  PART-1-1    33  0.016  0.00  0   \n",
       "...         ...        ...       ...       ...   ...    ...   ... ..   \n",
       "38011     20.00  Steel 310  0.000004  PART-1-1   829  0.004  0.01  0   \n",
       "38012     20.00  Steel 310  0.000004  PART-1-1   837  0.008  0.01  0   \n",
       "38013     20.00  Steel 310  0.000004  PART-1-1   845  0.012  0.01  0   \n",
       "38014     20.00  Steel 310  0.000004  PART-1-1   853  0.016  0.01  0   \n",
       "38015     20.00  Steel 310  0.000004  PART-1-1   861  0.020  0.01  0   \n",
       "\n",
       "        Temperature  T_left  ...  z/L  Tdiff_0.0005 (s)  scaled_T_left  \\\n",
       "0      3.000000e+02     300  ...    0              1.25      -1.341641   \n",
       "1      3.000000e+02     300  ...    0              1.25      -1.341641   \n",
       "2      3.000000e+02     300  ...    0              1.25      -1.341641   \n",
       "3      3.000000e+02     300  ...    0              1.25      -1.341641   \n",
       "4      3.000000e+02     300  ...    0              1.25      -1.341641   \n",
       "...             ...     ...  ...  ...               ...            ...   \n",
       "38011  3.127410e-34     600  ...    0               NaN       1.341641   \n",
       "38012  1.775720e-34     600  ...    0               NaN       1.341641   \n",
       "38013  1.301770e-34     600  ...    0               NaN       1.341641   \n",
       "38014  7.662100e-35     600  ...    0               NaN       1.341641   \n",
       "38015 -5.605190e-44     600  ...    0               NaN       1.341641   \n",
       "\n",
       "       scaled_T_right  scaled_T_bottom  scaled_T_top  scaled_time  \\\n",
       "0            0.000000             -1.0           1.0    -0.382321   \n",
       "1            0.000000             -1.0           1.0    -0.382321   \n",
       "2            0.000000             -1.0           1.0    -0.382321   \n",
       "3            0.000000             -1.0           1.0    -0.382321   \n",
       "4            0.000000             -1.0           1.0    -0.382321   \n",
       "...               ...              ...           ...          ...   \n",
       "38011       -1.224745              1.0          -1.0     5.589522   \n",
       "38012       -1.224745              1.0          -1.0     5.589522   \n",
       "38013       -1.224745              1.0          -1.0     5.589522   \n",
       "38014       -1.224745              1.0          -1.0     5.589522   \n",
       "38015       -1.224745              1.0          -1.0     5.589522   \n",
       "\n",
       "       scaled_location_x  scaled_location_y  scaled_alpha  \n",
       "0               -1.46385           -1.46385      1.469745  \n",
       "1               -0.87831           -1.46385      1.469745  \n",
       "2               -0.29277           -1.46385      1.469745  \n",
       "3                0.29277           -1.46385      1.469745  \n",
       "4                0.87831           -1.46385      1.469745  \n",
       "...                  ...                ...           ...  \n",
       "38011           -0.87831            1.46385     -1.135726  \n",
       "38012           -0.29277            1.46385     -1.135726  \n",
       "38013            0.29277            1.46385     -1.135726  \n",
       "38014            0.87831            1.46385     -1.135726  \n",
       "38015            1.46385            1.46385     -1.135726  \n",
       "\n",
       "[38016 rows x 25 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['scaled_T_left']   = StandardScaler().fit_transform(data[['T_left']])\n",
    "data['scaled_T_right']  = StandardScaler().fit_transform(data[['T_right']])\n",
    "data['scaled_T_bottom'] = StandardScaler().fit_transform(data[['T_bottom']])\n",
    "data['scaled_T_top']    = StandardScaler().fit_transform(data[['T_top']])\n",
    "data['scaled_time']     = StandardScaler().fit_transform(data[['Time (s)']])\n",
    "data['scaled_location_x'] = StandardScaler().fit_transform(data[['x/L']])\n",
    "data['scaled_location_y'] = StandardScaler().fit_transform(data[['y/L']])\n",
    "data['scaled_alpha']    = StandardScaler().fit_transform(data[['Alpha']])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((38016, 8), (38016,))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating independent and dependent variables, Temperature is the only dependent variable in our case\n",
    "x = data[['scaled_T_left', 'scaled_T_right', 'scaled_T_bottom', 'scaled_T_top', 'scaled_time', 'scaled_location_x', 'scaled_location_y', 'scaled_alpha']]\n",
    "y = data['Temperature']\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean MAE: 84.922\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# example of normalizing input and output variables for regression.\n",
    "from numpy import mean\n",
    "from numpy import absolute\n",
    "from numpy import loadtxt\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import HuberRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "# load data\n",
    "#dataset = loadtxt('housing.csv', delimiter=\",\")\n",
    "# split into inputs and outputs\n",
    "#X, y = dataset[:, :-1], dataset[:, -1]\n",
    "x = data[['scaled_T_left', 'scaled_T_right','scaled_T_bottom', 'scaled_T_top', 'scaled_time', 'scaled_location_x', 'scaled_location_y', 'scaled_alpha']]\n",
    "y = data['Temperature']\n",
    "# prepare the model with input scaling\n",
    "pipeline = Pipeline(steps=[('normalize', StandardScaler()), ('model', LR())])\n",
    "# prepare the model with target scaling\n",
    "model = TransformedTargetRegressor(regressor=pipeline, transformer=StandardScaler())\n",
    "# evaluate model\n",
    "cv = KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "scores = cross_val_score(model, x, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
    "# convert scores to positive\n",
    "scores = absolute(scores)\n",
    "# summarize the result\n",
    "s_mean = mean(scores)\n",
    "print('Mean MAE: %.3f' % (s_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "#storing starting time\n",
    "begin = time.time()\n",
    "\n",
    "#Creating Training-Testing data from the new data frame\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_x,test_x,train_y,test_y = train_test_split(x, y , test_size = 0.25, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Linear Regression and metric mean absolute error\n",
    "\n",
    "from sklearn.linear_model import LinearRegression as LR\n",
    "from sklearn.metrics import mean_absolute_error as mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "178.11400524500147\n"
     ]
    }
   ],
   "source": [
    "# Creating instance of Linear Regresssion\n",
    "lr = LR()\n",
    "\n",
    "# Fitting the model\n",
    "lr.fit(train_x, train_y)\n",
    "\n",
    "print(lr.intercept_) #Intercept of the Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime of Random Forest program (in seconds) is 5.188650369644165\n"
     ]
    }
   ],
   "source": [
    "#storing end time\n",
    "time.sleep(1)\n",
    "end = time.time()\n",
    "\n",
    "#Calculating runtime\n",
    "print (\"Total runtime of Random Forest program (in seconds) is\", end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 15))\n",
    "sns.heatmap(train_x.corr(), annot = True, cmap=\"RdYlGn\", vmin = -1, vmax = 1)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error 85.53069685939157\n"
     ]
    }
   ],
   "source": [
    "# Predicting over the Train Set and calculating error\n",
    "train_predict = lr.predict(train_x)\n",
    "k = mae(train_predict, train_y)\n",
    "print('Training Mean Absolute Error', k )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Absolute Error     83.48081914188829\n"
     ]
    }
   ],
   "source": [
    "# Predicting over the Test Set and calculating error\n",
    "test_predict = lr.predict(test_x)\n",
    "k = mae(test_predict, test_y)\n",
    "print('Test Mean Absolute Error    ', k )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.38165362,  12.86888876,  -0.67334219,   0.67334219,\n",
       "        15.64932187, -73.49597161, -76.18155126,  11.35640278])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Variables  Coefficient\n",
      "0      scaled_T_left    15.381654\n",
      "1     scaled_T_right    12.868889\n",
      "2    scaled_T_bottom    -0.673342\n",
      "3       scaled_T_top     0.673342\n",
      "4        scaled_time    15.649322\n",
      "5  scaled_location_x   -73.495972\n",
      "6  scaled_location_y   -76.181551\n",
      "7       scaled_Alpha    11.356403\n"
     ]
    }
   ],
   "source": [
    "#Regression Coefficients of the variables in order \n",
    "Coeff_data = {'Variables': ['scaled_T_left', 'scaled_T_right', 'scaled_T_bottom', 'scaled_T_top', 'scaled_time', 'scaled_location_x', 'scaled_location_y', 'scaled_Alpha'], 'Coefficient': lr.coef_}\n",
    "df = pd.DataFrame(Coeff_data, columns = ['Variables', 'Coefficient'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficient plot')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=120, facecolor='w', edgecolor='b')\n",
    "x = range(len(train_x.columns))\n",
    "y = lr.coef_\n",
    "plt.bar( x, y )\n",
    "plt.xlabel( \"Variables\")\n",
    "plt.ylabel('Coefficients')\n",
    "plt.title('Coefficient plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.269012785796136"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_x, train_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.894955891039"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_x,test_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[154.1923479  144.72078927 142.60311272 ... 194.75633582 -42.4823442\n",
      " 123.01551111]\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(test_x)\n",
    "print (y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5771</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>154.192348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27401</th>\n",
       "      <td>-8.259910e-35</td>\n",
       "      <td>144.720789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22628</th>\n",
       "      <td>2.732510e+01</td>\n",
       "      <td>142.603113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12451</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>166.985024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13111</th>\n",
       "      <td>8.743740e+01</td>\n",
       "      <td>286.096164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35221</th>\n",
       "      <td>2.681660e+02</td>\n",
       "      <td>333.322171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13384</th>\n",
       "      <td>9.986740e+01</td>\n",
       "      <td>25.413664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9154</th>\n",
       "      <td>1.324940e+02</td>\n",
       "      <td>194.756336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16235</th>\n",
       "      <td>-8.480660e-42</td>\n",
       "      <td>-42.482344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6065</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>123.015511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9504 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Actual   Predicted\n",
       "5771   1.000000e+02  154.192348\n",
       "27401 -8.259910e-35  144.720789\n",
       "22628  2.732510e+01  142.603113\n",
       "12451  1.000000e+02  166.985024\n",
       "13111  8.743740e+01  286.096164\n",
       "...             ...         ...\n",
       "35221  2.681660e+02  333.322171\n",
       "13384  9.986740e+01   25.413664\n",
       "9154   1.324940e+02  194.756336\n",
       "16235 -8.480660e-42  -42.482344\n",
       "6065   1.000000e+02  123.015511\n",
       "\n",
       "[9504 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':test_y, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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epEJNnDgRM+Odd95pct27776bBQsWNLleY5555hmOP/74zZatXLmSHj16sGzZ5jPnjRgxggceeKBZ25L0FPAiFWrs2LEcdthhjBs3rsl18w34dDp37swxxxzDQw89tHHZsmXLeP755xXgBaKAF6lAK1as4IUXXuDOO+/cIuCvv/569tlnH/bbbz8uvfRSJkyYQE1NDaeddhoDBw5k9erVVFdX8/HHYe7tmpoajjzySAD+85//cMghh7D//vtzyCGHMHPmzIzlGDVq1Gb7nzhxIsOGDaNTp05Zbeuqq67ihhs2TSq29957M2/ePADuu+8+DjzwQAYOHMh3vvMdGhrynamz/KibpEgp/eAH8Nprhd3mwIHwu99lXOWhhx5i2LBh7L777my77bZMnz6dQYMG8dhjj/HQQw8xdepUOnXqxJIlS9h222255ZZbuOGGGxg8eHDG7e655548++yzVFVV8eSTT3L55Zfzt7/9rdH1hw0bxtlnn83ixYvp0aMH48aN4/vf/35O20r29ttvM378eF544QXatWvHeeedx5gxYzjzzDOzenxcKOBFKtDYsWP5wQ9+AMDIkSMZO3YsgwYN4sknn+Sb3/wmnTqFOcS33XbbZm132bJljB49mlmzZmFmrFuXcUY52rdvzwknnMCECRM48cQTee211zjmmGNy2layp556imnTpjFkyBAAVq9eTa9evZr1XOJAAS9SSk3UtIth8eLFTJkyhTfffBMzo6GhATPj+uuvx92z6jJYVVXFhg0bADbrP37FFVdw1FFHMXHiRObNm7ex6SaTUaNGcc011+DuDB8+fOPZpNlsK7kcyWVxd0aPHs2vf/3rJvcfZ2qDF6kwEyZM4Mwzz+S9995j3rx5zJ8/n379+vH8889zzDHHcNddd7Fq1SoAlixZAkDXrl359NNPN26jurqaadOmAWzWbLJs2TJ69+4NhAOz2TjqqKOYNWsWt956K6NGjWrWtqqrq5k+fToA06dPZ+7cuQAMHTqUCRMm8NFHH218Hu+9V9Yj/+ZEAR9ns2fDu++WuhTSyowdO5avfvWrmy078cQTuf/++xk2bBgnnHACgwcPZuDAgRsPYH7jG9/g3HPP3XiQ9ec//zkXXnghn//852nbtu3G7fzkJz/hsssu49BDD836oGabNm048cQTWbx4MYcffniztnXiiSeyZMkSBg4cyG233cbuu+8OQP/+/bnmmms45phj2HffffniF7/Ihx+W3fzaeYvFnKyDBw92TfiRYuFC2HFH2G47qKsrdWkkydtvv81ee+1V6mJIinTvi5lNc/fMR5ZbMdXg4+qxx8Llxx9D1G1MRCqLAj6u/vvfTddfeql05RCRklHAx9Wbb8Kee4br0YEnEaksCvi4mjULBg2CXr0U8CIVSgEfR+6wYAH07g39+ingRSpUXgFvZsPMbKaZzTazS9Pc38HMxkf3TzWz6mh5DzN72sxWmNktKY9pb2Z3mNn/zOwdMzsxnzJWpKVLYe3a0Iumb1+owP6/IpJHwJtZW+BW4DigPzDKzPqnrHYWsNTddwVuAq6LltcDVwAXp9n0T4GP3H33aLv/zrWMFSsx6t9OO8H226ubpGyhbdu2DBw4cOPfvHnzqKmp4YILLgDCkLwvvvjixvUfeugh3nrrrWbvp0uXLlssO/LII3niiSc2W/a73/2O8847r9nbkszyqcEfCMx293fdfS0wDhiess5w4J7o+gRgqJmZu6909+cJQZ/qW8CvAdx9g7t/nEcZK1PihI4dd4SePWHZslCjF4lstdVWvPbaaxv/qqurGTx4MDfffDNQuIBPJ3UESYBx48ZtdharFEY+Ad8bmJ90uzZalnYdd18PLAN6NLZBM+seXf2lmU03swfNbPtG1j3HzGrMrKZONdTNpQY8qBYvTUpMpDFv3jxuv/12brrpJgYOHMi///1vJk2axI9//GMGDhzInDlzmDNnDsOGDeOAAw7g85///MZJQ+bOncvBBx/MkCFDuOKKK9Lu56STTuKRRx5hzZo1AMybN48FCxZw2GGHsWLFCoYOHcqgQYPYZ599ePjhhxstZ8L555+/cSiDadOmccQRR3DAAQdw7LHHbjx79eabb6Z///7su+++jBw5spAvW6uWz2Bj6UYkSj0tNpt1klUBfYAX3P0iM7sIuAE4Y4uNuN8B3AHhTNasSlwpFi8Olz17hl40EAK+d+r3r5RaiUYLZvXq1QwcOBCAfv36MXHixI33VVdXc+6559KlSxcuvji0op5wwgkcf/zxnHTSSUAY6+X2229nt912Y+rUqZx33nlMmTKFCy+8kO9+97uceeaZ3HrrrWn33aNHDw488EAef/xxhg8fzrhx4zj11FMxMzp27MjEiRPp1q0bH3/8MQcddBAnnHBCVgOgrVu3ju9///s8/PDD9OzZk/Hjx/PTn/6Uu+66i9/85jfMnTuXDh068Mknn2TzMsZCPgFfC+ycdLsPkDrlS2KdWjOrArYGlmTY5mJgFZD4tD1IaMeX5liyBNq0gW7dVIOXtBJNNLlYsWIFL774IieffPLGZYna+AsvvLBx8LEzzjiDSy65JO02Es00iYC/6667gDAK5OWXX86zzz5LmzZt+OCDD1i0aBE77LBDk+WaOXMmb775Jl/84hcBaGhoYMcddwRg33335bTTTmPEiBGMGDEip+ddjvIJ+FeA3cysH/ABMBL4eso6k4DRwEvAScAUzzD4jbu7mf0DOBKYAgwFCtPwV0mWLIFttgkhr4Bv1UowWnDeNmzYQPfu3Rv9gsimtj1ixAguuugipk+fzurVqxk0aBAAY8aMoa6ujmnTptGuXTuqq6s3G44YMg8RPGDAAF5Kc+b2o48+yrPPPsukSZP45S9/yYwZM6iqiv9o6Tm3wUdt6ucDTwBvAw+4+wwzu9rMTohWuxPoYWazgYuAjV0pzWwe8P+Ab5hZbVIPnEuAq8zsDULTzI9yLWPFWrIEEhM1bLdduEw024hkIXV44OTb3bp1o1+/fjz44INACNbXX38dgEMPPXTjAdQxY8Y0uv0uXbpw5JFH8q1vfWuLIYJ79epFu3btePrpp9MO8du3b1/eeust1qxZw7Jly3jqqacA2GOPPairq9sY8OvWrWPGjBls2LCB+fPnc9RRR3H99dfzySefsGLFinxenrKRVz94d5/s7ru7+2fd/dpo2ZXuPim6Xu/uJ7v7ru5+oLu/m/TYanff1t27uHsfd38rWv6eux/u7vu6+1B3fz+fMlak5IDfeutwmTJzvUgmX/nKV5g4cSIDBw7kueeeY+TIkfz2t79l//33Z86cOYwZM4Y777yT/fbbjwEDBmw8GPr73/+eW2+9lSFDhrCsic/cqFGjeP311zc76HnaaadRU1PD4MGDGTNmDHsmhttIsvPOO3PKKadsbHbZf//9gTA71IQJE7jkkkvYb7/9GDhwIC+++CINDQ2cfvrp7LPPPuy///788Ic/pHv37ltsN440XHAcDRkSmmYmTw63O3eG734XkiYnltLRcMGtk4YLlvKQXIMH6N4dKqjngIgECvg4Wro0HGRN6N5dTTQiFUgBHzfusHx56CKZoBp8qxOHptE4iev7oYCPm/p6aGiArl03Ldt6awV8K9KxY0cWL14c21ApN+7O4sWL6dixY6mLUnDx7whaaRJd25IDvnv3MD68tAp9+vShtrYWDbHRenTs2JE+ffqUuhgFp4CPm8YCXjX4VqNdu3b069ev1MWQCqAmmrhJF/Ddum1aLiIVQwEfN+kCvnNnWLMG1q8vTZlEpCQU8HGTLuATEyWsXNny5RGRklHAx02mgK+Q8TdEJFDAx40CXkQiCvi4aawNHhTwIhVGAR83iYBPnqBYbfAiFUkBHzeffgodO0LyZAZqohGpSAr4uPn0082bZ0ABL1KhFPBxo4AXkYgCPm7SBXziIKva4EUqigI+blSDF5GIAj5u0gV8+/bhoKsCXqSiKODjJl3Am4VavAJepKIo4OMmXcBDaIdXG7xIRVHAx01jAa8avEjFUcDHyYYNIcQV8CKCAj5eVq8Ol4lukckU8CIVRwEfJ6tWhct0Ad+5swJepMIo4OMkEfCdOm15X5cuOsgqUmEU8HHSVMCrBi9SURTwcaKAF5EkCvg4SRxkVcCLCAr4eMlUg+/cGdavhzVrWrZMIlIyCvg4SQT8VltteV8i9BO1fBGJPQV8nGSqwSdCP7GOiMSeAj5Osgl41eBFKoYCPk4yBbyaaEQqTl4Bb2bDzGymmc02s0vT3N/BzMZH9081s+poeQ8ze9rMVpjZLY1se5KZvZlP+SqOavAikiTngDeztsCtwHFAf2CUmfVPWe0sYKm77wrcBFwXLa8HrgAubmTbXwPUp6+5EgHfseOW9yngRSpOPjX4A4HZ7v6uu68FxgHDU9YZDtwTXZ8ADDUzc/eV7v48Ieg3Y2ZdgIuAa/IoW2VavTrU3s22vE8HWUUqTj4B3xuYn3S7NlqWdh13Xw8sA3o0sd1fAjcCGZPIzM4xsxozq6mrq2tOueNr1ar0zTOgNniRCpRPwKepJuI5rLNpZbOBwK7uPrGpnbv7He4+2N0H9+zZs6nVK8OqVen7wIOaaEQqUD4BXwvsnHS7D7CgsXXMrArYGliSYZsHAweY2TzgeWB3M3smjzJWlkw1eAW8SMXJJ+BfAXYzs35m1h4YCUxKWWcSMDq6fhIwxd0brcG7+23uvpO7VwOHAf9z9yPzKGNlySbg1QYvUjGqcn2gu683s/OBJ4C2wF3uPsPMrgZq3H0ScCdwr5nNJtTcRyYeH9XSuwHtzWwEcIy7v5X7UxG1wYtIspwDHsDdJwOTU5ZdmXS9Hji5kcdWN7HtecDe+ZSv4qxalX4+VtjUdVIBL1IxdCZrnGSqwbdpAx06KOBFKogCPk4S/eAbs9VWaoMXqSAK+DjJVIOHEPCqwYtUDAV8nDQV8J06KeBFKogCPk4ynegEqsGLVBgFfFw0NITp+NREIyIRBXxcZJpwO0EHWUUqigI+LjKNBZ+gNniRiqKAj4tME24nqIlGpKIo4OOiPhpaXwEvIhEFfFwkAj7dbE4JaoMXqSgK+LjIpgavNniRiqKAj4tsa/AKeJGKoYCPi0RwNxXw9fWwYUPLlElESkoBHxfZ1uCT1xWRWFPAx0U2Aa9JP0QqigI+LppTg1fAi1QEBXxcZNsPHhTwIhVCAR8X2dTgE/etWVP88ohIySng4yKbXjSJ+3SQVaQiKODjIhHa7ds3vo4CXqSiKODjor4+BLhZ4+t06LBpXRGJPQV8XNTXZz7ACqrBi1QYBXxcJGrwmSjgRSqKAj4uVq9WwIvIZhTwcaEavIikUMDHhQJeRFIo4ONCB1lFJIUCPi6aU4PXmawiFUEBHxfZHGStqoK2bVWDF6kQCvi4yKYGD2EdBbxIRVDAx4UCXkRSKODjQgEvIikU8HGRTS8aUMCLVBAFfFyoBi8iKRTwceCeXS8aCCNKKuBFKkJeAW9mw8xsppnNNrNL09zfwczGR/dPNbPqaHkPM3vazFaY2S1J63cys0fN7B0zm2Fmv8mnfBVj/XrYsEE1eBHZTM4Bb2ZtgVuB44D+wCgz65+y2lnAUnffFbgJuC5aXg9cAVycZtM3uPuewP7AoWZ2XK5lrBjZTNeXoIAXqRj51OAPBGa7+7vuvhYYBwxPWWc4cE90fQIw1MzM3Ve6+/OEoN/I3Ve5+9PR9bXAdKBPHmWsDNlMuJ2ggBepGPkEfG9gftLt2mhZ2nXcfT2wDOiRzcbNrDvwFeCpRu4/x8xqzKymrq6umUWPGdXgRSSNfAI+3dxwnsM6W27YrAoYC9zs7u+mW8fd73D3we4+uGfPnk0WNtaymXA7QQEvUjHyCfhaYOek232ABY2tE4X21sCSLLZ9BzDL3X+XR/kqR3Nr8BpsTKQi5BPwrwC7mVk/M2sPjAQmpawzCRgdXT8JmOLuGWvwZnYN4YvgB3mUrbKoiUZE0qjK9YHuvt7MzgeeANoCd7n7DDO7Gqhx90nAncC9ZjabUHMfmXi8mc0DugHtzWwEcAywHPgp8A4w3cwAbnH3P+dazoqgg6wikkbOAQ/g7pOBySnLrky6Xg+c3MhjqxvZbLp2e5XA5Z4AACAASURBVMlENXgRSUNnssZBcw+yrlsHDQ3FLZOIlJwCPg6aW4MHHWgVqQAK+DjIJeDVTCMSewr4OFDAi0gaCvg4aE4vmg4dNn+MiMSWAj4OVIMXkTQU8HGQ6EWTqJ1nooAXqRgK+Dior4f27aFNFm+nAl6kYijg4yDb6fpAAS9SQRTwcZDthNuggBepIAr4OMilBq8TnURiTwEfB9lOuA2qwYtUEAV8HKgNXkTSUMDHgQJeRNJQwMeBDrKKSBoK+DhQDV5E0lDAx0FzDrK2awdmCniRCqCAj4Pm1ODNNKuTSIVQwMdBcwIewpg1CniR2FPAx0FzA141eJGKoICPg+b0ogEFvEiFUMDHgWrwIpKGAr7cNTTAunUKeBHZggK+3DVnNqcEBbxIRVDAl7tcA16jSYrEngK+3DVnwu2Ejh03TfMnIrGlgC93qsGLSCMU8OUuURPXiU4ikkIBX+50kFVEGqGAL3cKeBFphAK+3OV6kFVt8CKxp4Avd4mA79Ah+8eoBi9SERTw5S6XGnziIKt7ccokIq2CAr7c5doGD7B2beHLIyKthgK+3OUT8GqmEYm1vALezIaZ2Uwzm21ml6a5v4OZjY/un2pm1dHyHmb2tJmtMLNbUh5zgJn9N3rMzWZm+ZQx9vIJeB1oFYm1nAPezNoCtwLHAf2BUWbWP2W1s4Cl7r4rcBNwXbS8HrgCuDjNpm8DzgF2i/6G5VrGipBLwCcOyKoGLxJr+dTgDwRmu/u77r4WGAcMT1lnOHBPdH0CMNTMzN1XuvvzhKDfyMx2BLq5+0vu7sBfgRF5lDH+1EQjIo3IJ+B7A/OTbtdGy9Ku4+7rgWVAjya2WdvENgEws3PMrMbMaurq6ppZ9Bipr4e2baGqKvvHKOBFKkI+AZ+ubTy131026+S0vrvf4e6D3X1wz549M2wy5po7mxMo4EUqRD4BXwvsnHS7D7CgsXXMrArYGljSxDb7NLFNSZZPwOsgq0is5RPwrwC7mVk/M2sPjAQmpawzCRgdXT8JmBK1rafl7h8Cn5rZQVHvmTOBh/MoY/zlEvA6yCpSEZrRcLs5d19vZucDTwBtgbvcfYaZXQ3UuPsk4E7gXjObTai5j0w83szmAd2A9mY2AjjG3d8CvgvcDWwFPBb9SWPURCMijcg54AHcfTIwOWXZlUnX64GTG3lsdSPLa4C98ylXRVHAi0gjdCZruVPAi0gjFPDlTgdZRaQRCvhyp4OsItIIBXy5UxONiDRCAV/uFPAi0ggFfLnLJeCrqsLwBgp4kVhTwJe7XAIeQju8DrKKxJoCvtzlGvCal1Uk9hTw5U4BLyKNUMCXOwW8iDRCAV/O1q+HhgYFvIikpYAvZ7nM5pSgg6wisaeAL2f5BLxq8CKxp4AvZwp4EclAAV/OEgGdGFumORTwIrGngC9nqsGLSAYK+HKmg6wikoECvpypBi8iGSjgy5kCXkQyUMCXMwW8iGSggC9n+bbBK+BFYk0BX87yrcGvXQvuhS2TiLQaCvhylm/Ag3rSiMSYAr6cFSLg1UwjElsK+HKmgBeRDBTw5Szfg6zJ2xCR2FHAl7P6emjTJkyi3VxqgxeJPQV8OUvM5mTW/MeqiUYk9hTw5SzX6fpAAS9SARTw5UwBLyIZKODLWT4BnzjIqjZ4kdhSwJcz1eBFJAMFfDlTwItIBgr4cqaAF5EMFPDlTAEvIhnkFfBmNszMZprZbDO7NM39HcxsfHT/VDOrTrrvsmj5TDM7Nmn5D81shpm9aWZjzSzHBKsAOsgqIhnkHPBm1ha4FTgO6A+MMrP+KaudBSx1912Bm4Drosf2B0YCA4BhwB/NrK2Z9QYuAAa7+95A22g9SUc1eBHJIJ8a/IHAbHd/193XAuOA4SnrDAfuia5PAIaamUXLx7n7GnefC8yOtgdQBWxlZlVAJ2BBHmWMt0LU4BXwIrGVT8D3BuYn3a6NlqVdx93XA8uAHo091t0/AG4A3gc+BJa5+z/T7dzMzjGzGjOrqaury+NplLF8Ar6qKvwp4EViK5+ATzcASur0QI2tk3a5mW1DqN33A3YCOpvZ6el27u53uPtgdx/cs2fPZhQ7RvIJeNC0fSIxl0/A1wI7J93uw5bNKRvXiZpctgaWZHjsF4C57l7n7uuAvwOH5FHGeMs34Dt21EFWkRjLJ+BfAXYzs35m1p5wMHRSyjqTgNHR9ZOAKe7u0fKRUS+bfsBuwH8ITTMHmVmnqK1+KPB2HmWMt0IEvGrwIrGVw0DigbuvN7PzgScIvV3ucvcZZnY1UOPuk4A7gXvNbDah5j4yeuwMM3sAeAtYD3zP3RuAqWY2AZgeLX8VuCP3pxdj69dDQ4MCXkQalXPAA7j7ZGByyrIrk67XAyc38thrgWvTLP858PN8ylUREk0rCngRaYTOZC1X+UzXl6CDrCKxpoAvV4UIeB1kFYk1BXy5KlTAqwYvElsK+HKlgBeRJijgy5UCXkSaoIAvVzrIKiJNUMCXKx1kFZEmKODLlZpoRKQJCvhypYAXkSYo4MuV2uBFpAkK+HJVqBr8unWwYUNhyiQirYoCvlwVKuBBB1pFYkoBX64KGfBqphGJJQV8uVLAi0gTFPDlavVqaNMmzKuaK028LRJrCvhytXo1bLUVWLrpbbOkGrxIrCngy9WqVSHg86GDrCKxpoAvV4kafD5UgxeJNQV8uVq9Gjp1ym8bCniRWFPAl6tC1OB1kFUk1hTw5UpNNCLSBAV8uSpkwOsgq0gsKeDLVSF70agGLxJLCvhypTZ4EWmCAr5cqReNiDRBAV+udJBVRJqggC9XaqIRkSYo4MtVIQK+bVto104BLxJTCvhytG4drF+ff8BD2MaqVflvR0RaHQV8OVq9Olzme5A1sY3E9kQkVhTw5SgRyIWqwSvgRWJJAV+OFPAikgUFfDkqdMCrDV4klhTw5SgRyIUIeLXBi8SWAr4cqYlGRLKQV8Cb2TAzm2lms83s0jT3dzCz8dH9U82sOum+y6LlM83s2KTl3c1sgpm9Y2Zvm9nB+ZQxlgrZi0YBLxJbOQe8mbUFbgWOA/oDo8ysf8pqZwFL3X1X4Cbguuix/YGRwABgGPDHaHsAvwced/c9gf2At3MtY2ypDV5EspBPDf5AYLa7v+vua4FxwPCUdYYD90TXJwBDzcyi5ePcfY27zwVmAweaWTfgcOBOAHdf6+6f5FHGeCpkwKsNXiS28gn43sD8pNu10bK067j7emAZ0CPDYz8D1AF/MbNXzezPZtY53c7N7BwzqzGzmrq6ujyeRhlSG7yIZCGfgLc0yzzLdRpbXgUMAm5z9/2BlcAWbfsA7n6Huw9298E9e/bMvtRxUMheNAp4kdjKJ+BrgZ2TbvcBFjS2jplVAVsDSzI8thaodfep0fIJhMAvX3ffDf36wVNPFW6bxajBe+p3s4iUu3wC/hVgNzPrZ2btCQdNJ6WsMwkYHV0/CZji7h4tHxn1sukH7Ab8x90XAvPNbI/oMUOBt/IoY2mtXAnnnQfz5sEFFxQuRAs9Fg1oREmRGMo54KM29fOBJwg9XR5w9xlmdrWZnRCtdifQw8xmAxcRNbe4+wzgAUJ4Pw58z90bosd8HxhjZm8AA4Ff5VrGknv00RDGp50Gb70Fr79emO2uXr1pqN98JX4FqJlGJHaq8nmwu08GJqcsuzLpej1wciOPvRa4Ns3y14DB+ZSr1Xj6aejWDa69FsaMgeeeg4ED899uIcaCT1DAi8SWzmQtpldegcGDoW9f6NMHXnyxMNtVwItIFhTwxbJ2LbzxBgwZEm4PGQKvvVaYba9aVfiA18lOIrGjgC+WOXPCzEt77x1u77UXzJoVgj9fhazBJw6yqgYvEjsK+GJ5551wueee4bJ/f2hogNmz89/26tWF6UEDaqIRiTEFfLHMnBku94h6fCaCPhH8+VAbvIhkQQFfLHPmwPbbQ9eu4XZ1dbh8//38t12MgFcbvEjsKOCL5f33YZddNt3edlvo3Bneey//bRfyIKva4EViSwFfLPPnbx7wZuF2a63BK+BFYkcBXwzuIch33nnz5X37Fi7gdZBVRJqggC+GTz4J49Ak1+Ah3C5EE43a4EUkCwr4YkjU0lNr8LvsAnV1+deWCxnwHTtu2qaIxIoCvhjmR3OZpNbg+/YNl/k007gXNuDbtAkhr4AXiR0FfDFkqsEn35+LdevCCVOFCnjQpB8iMaWAL4b588NQvttvv/nyRMDn0w5fyMk+EjTxtkgsKeCLYf586N07NH8k23HHcLlwYe7bLuRkHwmaeFsklhTwxbBoEeyww5bLO3SAbbYpTMCriUZEmqCAL4a6OmhsIvAddsgv4FeuDJeFrMEr4EViSQFfDC0R8J07576NVGqDF4klBXyhuWcO+O23b30BrzZ4kVhSwBfa8uWhK2OmGvyiRblvv1g1eAW8SOwo4Autri5cZgr4FSvCXy4U8CKSJQV8oWUT8JB7LV5t8CKSJQV8oWUb8Lm2w6sNXkSypIAvtHIMeDXRiMSSAr7Qmgr4xPAF+QS82aZRIAthq61g7dowxo2IxIYCvtDq6kKTR2MnIvXsGQI6n4Dv3Dlso1A06YdILCngCy1TH3iAtm1hu+021fSbKxHwhaR5WUViSQFfaE0FPECvXvDRR7ltvxgBrxq8SCwp4AutnANeXSVFYkUBX2jlGPCJ7SV66IhILCjgCy2bgO/Zs3W1wXfpsmnbIhIbCvhCWrkytGNnU4P/5JPQNTGXfagGLyJZUMAXUlN94BN69dp8/eYoZsDnOj6OiLRKCvhCam7A59IOryYaEcmSAr6QyjXg1UQjEkt5BbyZDTOzmWY228wuTXN/BzMbH90/1cyqk+67LFo+08yOTXlcWzN71cweyad8LS7bgE/c39yAd1cTjYhkLeeAN7O2wK3AcUB/YJSZ9U9Z7SxgqbvvCtwEXBc9tj8wEhgADAP+GG0v4ULg7VzLVjLFboNfswY2bCheP3jV4EViJZ8a/IHAbHd/193XAuOA4SnrDAfuia5PAIaamUXLx7n7GnefC8yOtoeZ9QG+DPw5j7KVRl0dtG8PXbtmXm/rraFdu+bX4IsxkiRAmzZhmwp4kVjJJ+B7A/OTbtdGy9Ku4+7rgWVAjyYe+zvgJ8CGTDs3s3PMrMbMaupy7VNeaIk+8E0NBGaW28lOxQr4xDYV8CKxkk/Ap0sxz3KdtMvN7HjgI3ef1tTO3f0Odx/s7oN7NtUk0lKyOckpoTUGvNrgRWIln4CvBXZOut0HWNDYOmZWBWwNLMnw2EOBE8xsHqHJ52gzuy+PMrascg74Ll1UgxeJmXwC/hVgNzPrZ2btCQdNJ6WsMwkYHV0/CZji7h4tHxn1sukH7Ab8x90vc/c+7l4dbW+Ku5+eRxlbVnMCPpfhChI17ES/9UJSE41I7FTl+kB3X29m5wNPAG2Bu9x9hpldDdS4+yTgTuBeM5tNqLmPjB47w8weAN4C1gPfc/fyn06o2DX4Tz8Nl926Ne9x2VATjUjs5BzwAO4+GZicsuzKpOv1wMmNPPZa4NoM234GeCaf8rWoNWtCACe6QDalV68wPG9z+rUvXx4um+qlk4vOnXMfAE1EWiWdyVoo2faBT8jlbNZEDb4YAa82eJHYUcAXSksGvJpoRCQLCvhCyTXgm9Mssnx56EOvfvAikgUFfKE0N+BzGY/m009D80xTJ1LlItFE46mnMpSntWvhww81C6FUNgV8obREwC9fXpz2dwg1eHeory/O9ots+XIYMwbOOAOqq8PwOjvtFJ7WNtvAKaeE+3OZY0WkXOXVi0aS1NVBVRV0757d+p07h7/m1uCL0f6eKA+EdvjE4GOtnDs8/zz86U/w97+HybS22w6OPhrOPBN22AGWLYPZs+Gxx+DBB+FnP4Mrr4TRo8MQPCJxpoAvlLq6kC7NaT5pbl/4YtfgITTTtJahHxqxYkWojd96K/z3v2HsttGjQ6h/7nPpg3vDBnj8cfj5z+Fb34J774W77gq1fZG4Uh2mUJpzklNCcwM+0QZfDGUwq9OCBfCTn0CfPnDuudC2Lfzf/4Xlt90GBx/ceK28TRv40pfgP/+BP/8Zampgn33g4Ydb9jmItCQFfKF89FHzA765wxUsX94yTTStzMyZcPbZ0K8f3HgjHHssvPACTJ8elnfqlP22zOCss0LNf8894atfhV/9KjbHlkU2o4AvlLjU4FtRwL//fmh22Wuv0CRz9tkwaxaMHw+HHJJfZ6K+feHZZ2HUKPjpT+G000IbvkicqA2+UPIJePfs0qqYB1kT202cTFVCa9fCddfBtdFAFj/+MVx8ceEPDWy1Fdx3X2iqufzy8OXxyCOw/faF3Y9IqagGXwjr1sEnn+QW8OvXh8c2xb24B1kTAZ8Y76ZEpk+H/fcPPV2GD4f//S+EfbGO+5rBpZfCQw/BW2/BoYfCu+8WZ18iLU0BXwgffxwucwl4yK6Zpr4eGhqKX4MvUcC7h14xBx8cujY+8khoitlll5bZ/wknwFNPwdKlIeTfeKNl9itSTAr4QmjuSU4JzRmuoJgjSUJJA37ZsnAi0vnnwxe+AK+9Bl/+cosXg4MOgueeC71zDj88XBcpZwr4Qsg14JtzNmsxBxoD6NAhTBjewgH/xhswaBBMnAjXXw//+Ec4naBU+veHF18MJ0kdc0w4QUqkXCngCyHfGnw2AV/sGjyEL48WPMg6aVLoDVNfH3q0/PjHrePs0l12CbX3vfYKxwH+9rdSl0gkN63g3ykGcg34RFW1OTX4YgZ8164tUoN3h9/+FkaMCCH6yish6FuTnj1hyhQYMiQ0H917b6lLJNJ8CvhCqKsL3TG23bZ5j2vfPoyE1ZwafLGaaBLbLnLAr10bTjT6yU/g5JPh3/8Og4K1Rt27wxNPwJFHhv74t99e6hKJNI8CvhDq6qBHj3B0rrmyPdlp2bJwWcYBv3x5OHj6l7+EbpBjxzbvLNRS6NIFHn0Ujj8evvvdcCatSLnQiU6FkMtJTgm9emXXi2bp0nDZ3F8JzdGtWxhEvQgWLoTjjgtDBNx9dxgcrFx07BhGqzz99HDC1YoV4QuqGMPyixSSAr4Q8gn4nj3hnXeaXi8R8NkOR5yLbt3CwC8F9r//hfFj6upC//Zhwwq+i6Jr1w7uvz/84rjqqnBI5Le/VchL66aAL4S6utC/Lhe9eoUuJE1ZsiQcBG3XLrf9ZKMIvWimTg3NG2bw9NPhoGW5atsW7rwzNNvceGMYePPWW1tHzx+RdBTwhZBvE83ixWHIgqoMb8fSpeGAbDEVuA3+0UdDD5QddggHK3fdtWCbLpk2beDmm0PI/+Y3IeTvuivzWydSKvpY5quhIQR0PgHvHraRaZSrlgj4rl3DkIrr1uX9S+Evf4Fvfxv22w8mT47XAF5m8Otfh5frpz8N877ef3/oFCXSmujHZb4WLw4BnThpqbmyHa5gyZLiHmCFgowo6Q7XXBNmTRo6FJ55Jl7hnuzyy+Gmm8KJUCNGaLhhaX0U8PlatChc5ppi2Q5X0FJNNJBzM01DA3zve3DFFaHHyT/+UdzzslqDH/wgzCr1+OOhl1CiN6tIa6CAz1e+AZ/tcAWtPOBXrw7t7bfdFk5iuueeymmyOPvsMCHJCy+EkSjff7/UJRIJFPD5aqmAb8VNNEuXhoG5Jk6E3/8+jN9eaT1LRo0KB5Jra8PE39Onl7pEIgr4/CWCOdeA33bbkIaZAn71alizplXW4OfPh89/PkxmPW4cXHBBkcpWBo4+OtTi27cPww0/+mipSySVTgGfr0WLwn/01lvn9vg2bZqefDtxklNLBXw2M0wBr78exlCfPz/UXk85pYhlKxMDBsDLL8Mee4RJRG67rdQlkkqmgM/XokWhmSWfUxqbGo+mJYYpSN5+Yn8Z/Otfoebepk2otR55ZHGLVk523DEMovalL8F554VhkDdsKHWppBIp4PO1aFH+/QB79swc8EuWhMti1+AT228i4O++O4RXv36htrr33sUtVjnq0iUckzjvPLjhBhg5Ut0opeUp4PNViIDPtgZf7IBv3x46d970hZLCHX7xC/jmN0ON/bnnoHfv4hapnFVVwS23hIB/8MEwHWFi+l6RlqCAz1ecAh5CM02agF+3LnQHvOqqMDb6o48Wd+TiuDCDH/0oBPz06WFS8VmzSl0qqRQK+Hy4h2AuRMAvXx56yqSTqPb16JHffrKxzTZbNNF8+il85SthzJUrrghNNJXSx71QTjopzBD1ySch5F94odQlkkqggM/H0qWhaluIgIfGe9IsWhQmxc61p05zpNTgFywIXf6efDKcsXn11RoiN1cHHwwvvRRe4qFD4YEHSl0iibu8At7MhpnZTDObbWaXprm/g5mNj+6fambVSfddFi2faWbHRst2NrOnzextM5thZhfmU76iy/ckp4SmTnZauDAMydgSyZoU8DNmhFCaPTuM43722cXffdztumsI+cGD4dRT4frrww9BkWLIOeDNrC1wK3Ac0B8YZWapg6KfBSx1912Bm4Drosf2B0YCA4BhwB+j7a0HfuTuewEHAd9Ls83WIxHwuQ40ltDUeDQLF7bciF1RwD/zTDjtfu3aMFx9OU7S0Vr16BF+EZ16KlxySehp09BQ6lJJHOVTgz8QmO3u77r7WmAcMDxlneHAPdH1CcBQM7No+Th3X+Puc4HZwIHu/qG7Twdw90+Bt4HW20+jpWrwixaFGnxL2GYbxn78RY49NkyG/fLLsP/+LbPrStKxYxhi+JJLwmTeZ54ZpgQQKaR8Ar43MD/pdi1bhvHGddx9PbAM6JHNY6PmnP2Bqel2bmbnmFmNmdXUZTOnaTHkO0xBQiK8FyxIf3+iiaYF3Pz2F/n6uns4+HMNvPAC9O3bIrutSG3ahElDfvWrEPZf/3o4pCNSKPkEfLoG4dTWxMbWyfhYM+sC/A34gbunHRjF3e9w98HuPrhnrpNt5GvRojCPW769W7p0Cdt4770t71u/Phx8bYGA//Wv4cJHvsjX+BtP3L2wRXplClx2WZgC8MEHw3APjXWmEmmufAK+Ftg56XYfILUKunEdM6sCtgaWZHqsmbUjhPsYd/97HuUrvkWLQvt5IYZO7NsX5s3bcnldXTgKV8SAd4ef/SxMYHH6599jPKfSYWX6k52kOC66CP7wB3joIfja16C+vtQlkjjIJ5leAXYzs35m1p5w0HRSyjqTgNHR9ZOAKe7u0fKRUS+bfsBuwH+i9vk7gbfd/f/lUbaWUcimk+rq9DX4hQvDZZEC3j2Ey7XXhin27rliNlU0ZDUejRTW+efDn/4Upjj86lcV8pK/nAM+alM/H3iCcDD0AXefYWZXm9kJ0Wp3Aj3MbDZwEXBp9NgZwAPAW8DjwPfcvQE4FDgDONrMXov+vpRrGYuutrZw5+onavCpfeYSAV+EXjQbNsC558LvfgcXXhjCpc120YBjjQxXIMV1zjnw5z+H0TmHD9f4NZKfvCbddvfJwOSUZVcmXa8HTm7ksdcC16Yse5707fOt0wcfhNkdCqG6Ovw319Vt3u0y0VOnwDX49evDmDL33Rcmjv7lL6Nu9omGdwV8yZx1Vngvzj47hPzDD8NWW5W6VFKOdCZrrurrQxj36VOY7VVXh8vUZpoi1ODXrg2jG953X2iaueaapHOottsuXGpUrJL61rfC0BBPPhnGlV+1qtQlknKkgM9VoktjIZtoYMsDrfPnQ/fuYZTHAli9OrTv/u1voWnm8stTVujSBTp12vTLQUrmG9+Av/wFnnpKIS+5yauJpqJ98EG4LFQNvrGAnzs3DLxeAEuWhKB48UW4445wUDWtHXbY9MtBSmr06NBJa/RoOP54+Mc/CvZdLxVANfhc1daGy0LV4Lt3D33hU8eSLVDA19aGGZheeQXGj88Q7hCagxTwrcYZZ8Bf/xpmiTr+eFi5stQlknKhgM9VoWvwAHvuCe+8s+m2e6jRJ9rnc/TWW3DIISHkH38cTk572DvJDjuoiaaVOf10uPfeMC7Ql77UrHnRpYIp4HNVWwtduxZ21ou99oK33950e8GCcDD3s5/NeZMvvgiHHRZOgf/3v+Goo7J4kGrwrdLXvw5jxoT39Igj4MMPS10iae0U8LkqZB/4hP79Q++VRO15xoxNy3Pwj3+EaeK22y6EwsCBWT5whx1g8WINjNIKjRwZ3tdZs8Kvsv/9r9QlktZMAZ+r+fML2zwDMGhQuJw+PVwmAn7AgGZv6q67Qm+ZvfcOswc1qxk/0ec+0zSCUjLDhsHTT4e2+EMPDcdVRNJRwOeqgL1bNkqMyzttWrh8440w1k0zBlNzD33bzzor1N6nTGnWw4NEn3u1w7daQ4aEL+6uXcME6I89VuoSSWukgM/FihXhJKfPfKaw2+3WLTTHPP98uP3CC2FKpSw1NMAFF4SBw04/HSZNCt3amy1Rg1c7fKu2226h6W3PPcOcuX/5S6lLJK2NAj4Xc+eGy0LX4AGOOSYcDZ0zJzS0HnZYVg9bswZGjYJbboGLL4Z77sljYuxEDV4B3+rtsAM880yY4/Vb3wpnJWsKQElQwOfi3XfDZaFr8BDORKqv39Td5StfafIhy5bBcceF8cRvuAF++9s8RzBWwJeVrl3DgdczzoArrtAUgLKJzmTNRaIGX4yAP/LI0MD6yivhaNqee2Zc/cMPQ7jPmBHGljnttAKUoVOnMOhYoq+/tHrt24dfbX36hIlbPvwwzBLVqVOpSyalpIDPxbvvhmrTttsWfttm4WykBx8MHZ8zmDUrtOjU1cEjj8CxxxawHNXVm77IpCyYhen/dtopHIv5whfg0UfRzFwVTE00uZg7N9TerUgjG2+7LXznO+FLpBGvvBL6Qa9YEbrMFTTcIQR8uhmmpNU7//xQP5g2LbT0qbdr5VLA52LOnOIcYM3SE0+Ef9yuXUMviiFDirCTfv3ST0AiZeHEE8OvulmzowbSCwAAC4NJREFU4PDDw2kbUnkU8M21bh3Mng177FGS3Y8ZEwac2nXXEO677VakHSUmIFH1r2x98YuhMvDhh2GguTlzSl0iaWkK+OaaNSuEfA5nl+brxhtD//bPfz70pCziPNybfqGomaasHXZYONltxYrwuUmcHC2VQQHfXIn/kL33brFdbtgQ+rZffHEYCfKxx2DrrYu808QIljrQWvYOOCBUCCAMUpY4UVriTwHfXDNmhE7mTXRfLJS1a8NkDzfeGA6ejR0LHTq0wI4TAa8afCwMGADPPRfObD766E0nS0u8KeCba8aM0IOmBWZBXro0dIVPzJ16883Qtm3Rdxt06RKGoUyc1CVl77OfDcG+446he+0//1nqEkmxKeCba8aMFml/nzs3dIN8/vkwm8/llxevV2aj+veHN99s4Z1KMfXpEyYN2X33cJL0xImlLpEUkwK+OVauDANw77NPUXczdSp87nNhMMd//Sucgl4S++4L//1vOAggsdGrVzh3YtCgcEznvvtKXSIpFgV8c9TUhEE+DjqoaLsYOzaMVtC1K7z0UjgoVjL77Re6X+hAa+xss02oPBxxRKhAXHmlvsfjSAHfHC++GC6LEPDr14deMl//ejhx6eWXS9bVfpP99guXb7xR2nJIUXTpEoYy+OY34Ze/hBEjNNdr3Cjgm+Oll0Lq9uhR0M0uXhwGDEv0lHnqqRwm6SiGAQNCj6HXXy91SaRIOnaEO++EP/wBJk8OTYOaBjA+FPDZcg8B34wJOLIxfXqosT/7bJhm7w9/gHbtCrqL3HXqFE6VffXVUpdEisgsVCyefDJMCTx4cDiwr1Eqyp8CPluvvx4+/YcfXpDNuYcwP/jgcGLss8+Gn8qtTuK02fXrS10SKbIjjwwnQQ0cGM69OOmk8JGX8qWAz9bEiaG54vjj897U0qVhMKgLLgjjhbz2Wvhp3Codc0yYUUQzO1eEXXYJPWyuvz5MIrL33qGdXsqTAj5bDz0UprDPs3H82WdD97R//CPMvjRpUsGb9Atr6NDwxfbEE6UuibSQtm3hxz8O3+m9eoU6zde+ppOay5ECPhszZ4aeJCNG5LyJlStDjf2II0JePvcc/OhHeU6t1xK23TYcJFA1ruLst18I+V/9Kny/77UX/OIXYZBRKQ+tPV5ah1tuCUc+m5hhqTH//nc4Z+gPfwgh/8YbRe1KX3innBLOAfjvf0tdEmlhHTrAZZfBO+/A8OFw1VWhI9kf/ximDpbWTQHflLlz4f/+L4zT28zxeT/4AM48Mxy8MgtB//vfQ+fOxSlq0YweHQp97bWlLomUyM47w7hx8Mwz4fr3vhdGlL7xxnAunLROCvjGrFgRuhCcckqoxvziF1k/dNGi0Ia5++7wwANw6aWh1l6gDjgtr0eP0J40fnzoPycV64gjwvhITz8dTpO4+OIQ+OeeG3oRq2tl62Ieg3dk8ODBXlNTU7gNukO3biHk27WDCRPghBOafEhNDdx+exhuYM0aGDUqnCFYwtn9Cmft2jC05csvhxEmizrbiJSLl18OLZh//3tom99999CSeeyxoT99VVWpS5gfM5vm7oNLXY5c5RXwZjYM+D3QFvizu/8m5f4OwF+BA4DFwKnuPi+67zLgLKABuMDdn8hmm+kUPODXrw9tKZ9+GkZjamT0yJUrw+gF//pX+IDPmRNaMk47LVR4d9+9cEVqFVavDic9HXJIqUsircynn4Z60D33bJpcpFu3MHfwEUeE7pYDBoShilt8VNQ8VGzAm1lb4H/AF4Fa4BVglLu/lbTOecC+7n6umY0Evurup5pZf2AscCCwE/AkkIjDjNtMpxAB7x7+NmwI+b5qVajAr1wZxudYsABqa0O7+nvvheON77wTxh6rqgq9CU88MbToFH22JZFWrK4uNOE89VQ4OzZ5SoHu3UPFZ4cdwt/224dpBzp3DidOd+q06XrHjuEHdLt24X8s0/VizZNQ7gGfzw+oA4HZ7v4ugJmNA4YDyWE8HLgquj4BuMXMLFo+zt3XAHPNbHa0PbLYZl5efTWcnNnQEMI8+S8b7dqFMbUHDICvfjVs65BDwsBNIhJOFTnllPAHYd72GTM2/c2eHfouvPxy+DIoRCuxWQh5s83/EveZhS+XDz7If1/lJJ+A7w3MT7pdC6Sej7lxHXdfb2bLgB7R8pdTHts7ut7UNgEws3OAc6KbK8xsZg7PodnWrQsfzrlz4ZFHANgOaE0ndLe28kDrK5PKk1nZlce96dE0Vq3KqXmob7Mf0YrkE/DpXqrU7+LG1mlsebpePWm/3939DuCOTAVsCWZW05p+wrW28kDrK5PKk5nKEx/5dJOsBXZOut0HWNDYOmZWBWwNLMnw2Gy2KSIiWcgn4F8BdjOzfmbWHhgJTEpZZxIwOrp+EjDFw1HdScBIM+tgZv2A3YD/ZLlNERHJQs5NNFGb+vnAE4QujXe5+wwzuxqocfdJwJ3AvdFB1CWEwCZa7wHCwdP1wPfcvQEg3TZzf3otouTNRClaW3mg9ZVJ5clM5YmJWJzoJCIiW9JQBSIiMaWAFxGJKQV8M5jZyWY2w8w2mNnglPsuM7PZZjbTzI5NWj4sWjbbzC4tcvlabF9J+7zLzD4yszeTlm1rZv8ys1nR5TbRcjOzm6PyvWFmg4pQnp3N7Gkzezt6ry4sZZnMrKOZ/cfMXo/K84toeT8zmxqVZ3zUqYCo48H4qDxTzay6kOVJKldbM3vVzB4pdXnMbJ6Z/dfMXjOzmmhZyT5DseLu+svyD9gL2AN4BhictLw/8DrQAegHzCEcJG4bXf8M0D5ap3+RytZi+0rZ7+HAIODNpGXXA5dG1y8Frouufwl4jHAexEHA1CKUZ0dgUHS9K2Hoi/6lKlO03S7R9XbA1Gg/DwAjo+W3A9+Nrp8H3B5dHwmML9L7dhFwP/BIdLtk5QHmAdulLCvZZyhOfyUvQDn+pQn4y4DLkm4/ARwc/T3R2HoFLlOL7SvNvqtTAn4msGN0fUdgZnT9T4SxhbZYr4hle5gwtlHJywR0AqYTzs7+GKhKfe8Sn53oelW0nhW4HH2Ap4CjgUeisCxledIFfMnfrzj8qYmmMNIN29A7w/KWLEMpbO/uHwJEl72i5S1axqg5YX9CrblkZYqaQ14DPgL+Rfil9Ym7J06uT97n/2/n/FmjCMIw/ntAMeIfgqIQsJB8ALEUYpEiCB4iWAta+AWsBAn4EcTG0lIsRIOt/79A0GgkirGThKRSO7F4LGYW9+QuRNm73RveHwy7N7fLPNwOL+++M/f02XsAlb1Hk9wBbgCVA9PRlvUYeCppOVuQQEfm0KQz4W7NzSPpOTDI7HzR9pNhtw3o+yfrhQbYjXVE24xNo6SDwCPguu0fGm5CMnJNTv/xOC1pGlgilfqGjTlSPZIuANu2lyXN72LMcTyzOdsbko4DzyR93OHaSZjnnSEC/F/YXviP23ayWBiX9UKXbB62JM3Y3pQ0Q8pcYUwaJe0lBff7th93QROA7W+SXpNqx9OS9uSsuD5mpeer+u09mmIOuCipB0wBh0kZfVt6sL2Rj9uSlkjOsq0/rxKIEk0zdMF6oUs2D3WLiqukOnjVfyXvhDgDfK9ew5tCKVW/B6zZvt22JknHcuaOpP3AArAGvCLZdwzSM8jeoxFs37R9wvZJ0hx5aftyW3okHZB0qDoHzgGrtDiHiqLtRYBJasAlUgbxE9iif1FzkVRb/QScr/X3SDs5vpDKPKPUN7axamM+ADaBX/m3uUaq0b4APufjkXytgLtZ33tqC9UN6jlLemV/B7zNrdeWJuAU8CbrWQVu5f5ZUhKwDjwE9uX+qfx5PX8/O8JnN8+fXTSt6MnjruT2oZq3bc6hklpYFQRBEBRKlGiCIAgKJQJ8EARBoUSAD4IgKJQI8EEQBIUSAT4IgqBQIsAHQRAUSgT4IAiCQvkNnwAah4v+GEAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(5, 7))\n",
    "\n",
    "\n",
    "ax = sns.distplot(y, hist=False, color=\"r\", label=\"Actual Value\")\n",
    "sns.distplot(y_pred, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax)\n",
    "\n",
    "\n",
    "plt.title('Actual vs Fitted Values for Temperature (Linear Regression)')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Analytical Solution to create features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking Negative Natural Logarithm values of Diffusion Coefficient\n",
    "#data['negLog_DC'] = np.log(data['Diffusion Coeff'])*(-1)\n",
    "\n",
    "data['exp t1'] = np.exp((-10)* data['Time (s)'])\n",
    "\n",
    "data['exp t2'] = np.exp((-40)* data['Time (s)'])\n",
    "\n",
    "data['exp t3'] = np.exp((-90)* data['Time (s)'])\n",
    "\n",
    "data['sq_x/L'] = np.square(data['x/L'])\n",
    "\n",
    "data['sq_y/L'] = np.square(data['y/L'])\n",
    "\n",
    "data['Tl-Tr'] = data['T_left'] - data['T_right']\n",
    "data['Tb-Tt'] = data['T_bottom'] - data['T_top']\n",
    "\n",
    "data['Tl+Tr'] = data['T_left'] + data['T_right']\n",
    "data['Tb+Tt'] = data['T_bottom'] + data['T_top']\n",
    "\n",
    "data['(sq_x/L)-x/L'] = data['sq_x/L'] - data['x/L']\n",
    "data['(sq_y/L)-y/L'] = data['sq_y/L'] - data['y/L']\n",
    "\n",
    "data['deltaT_x/L'] = data['Tl-Tr'] * data['x/L']\n",
    "\n",
    "data['Timespacex1'] = data['(sq_x/L)-x/L'] * data['Tl+Tr'] * data['exp t1'] \n",
    "data['Timespacey1'] = data['(sq_y/L)-y/L'] * data['Tb+Tt'] * data['exp t1'] \n",
    "\n",
    "data['Timespacex2'] = data['(sq_x/L)-x/L'] * data['Tl-Tr'] * data['exp t2']\n",
    "data['Timespacey2'] = data['(sq_y/L)-y/L'] * data['Tb-Tt'] * data['exp t2'] \n",
    "\n",
    "data['Timespacex3'] = data['(sq_x/L)-x/L'] * data['Tl+Tr'] * data['exp t3']\n",
    "data['Timespacey3'] = data['(sq_y/L)-y/L'] * data['Tb+Tt'] * data['exp t3'] \n",
    "#data['DCParameter'] = (data['Diffusion Coeff'] * data['Time(s)'] * data['Tl-Tr']) / (data['sq_x/L'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Time (s)</th>\n",
       "      <th>Material</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Part</th>\n",
       "      <th>Node</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>T_left</th>\n",
       "      <th>...</th>\n",
       "      <th>Tb+Tt</th>\n",
       "      <th>(sq_x/L)-x/L</th>\n",
       "      <th>(sq_y/L)-y/L</th>\n",
       "      <th>deltaT_x/L</th>\n",
       "      <th>Timespacex1</th>\n",
       "      <th>Timespacey1</th>\n",
       "      <th>Timespacex2</th>\n",
       "      <th>Timespacey2</th>\n",
       "      <th>Timespacex3</th>\n",
       "      <th>Timespacey3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38011</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>829</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>3.127410e-34</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>-1.328541e-85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38012</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>837</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1.775720e-34</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>-1.992811e-85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38013</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>845</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1.301770e-34</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-1.992811e-85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38014</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>853</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>7.662100e-35</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>-1.328541e-85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38015</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-1</td>\n",
       "      <td>861</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.605190e-44</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time (s)   Material     Alpha      Part  Node      X     Y  Z  \\\n",
       "38011      20.0  Steel 310  0.000004  PART-1-1   829  0.004  0.01  0   \n",
       "38012      20.0  Steel 310  0.000004  PART-1-1   837  0.008  0.01  0   \n",
       "38013      20.0  Steel 310  0.000004  PART-1-1   845  0.012  0.01  0   \n",
       "38014      20.0  Steel 310  0.000004  PART-1-1   853  0.016  0.01  0   \n",
       "38015      20.0  Steel 310  0.000004  PART-1-1   861  0.020  0.01  0   \n",
       "\n",
       "        Temperature  T_left  ...  Tb+Tt  (sq_x/L)-x/L  (sq_y/L)-y/L  \\\n",
       "38011  3.127410e-34     600  ...    400         -0.16           0.0   \n",
       "38012  1.775720e-34     600  ...    400         -0.24           0.0   \n",
       "38013  1.301770e-34     600  ...    400         -0.24           0.0   \n",
       "38014  7.662100e-35     600  ...    400         -0.16           0.0   \n",
       "38015 -5.605190e-44     600  ...    400          0.00           0.0   \n",
       "\n",
       "       deltaT_x/L   Timespacex1  Timespacey1  Timespacex2  Timespacey2  \\\n",
       "38011       120.0 -1.328541e-85          0.0         -0.0          0.0   \n",
       "38012       240.0 -1.992811e-85          0.0         -0.0          0.0   \n",
       "38013       360.0 -1.992811e-85          0.0         -0.0          0.0   \n",
       "38014       480.0 -1.328541e-85          0.0         -0.0          0.0   \n",
       "38015       600.0  0.000000e+00          0.0          0.0          0.0   \n",
       "\n",
       "       Timespacex3  Timespacey3  \n",
       "38011         -0.0          0.0  \n",
       "38012         -0.0          0.0  \n",
       "38013         -0.0          0.0  \n",
       "38014         -0.0          0.0  \n",
       "38015          0.0          0.0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['x/L'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Location_X', fontsize=14)\n",
    "plt.xlabel('Location_X', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Timespacex1'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Timespace X Parameter 1', fontsize=14)\n",
    "plt.xlabel('Timespacex1', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Timespacex2'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Timespace X Parameter 2', fontsize=14)\n",
    "plt.xlabel('Timespacex2', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Timespacex3'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Timespace X Parameter 3', fontsize=14)\n",
    "plt.xlabel('Timespacex3', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((38016, 9), (38016,))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating independent and dependent variables, Temperature is the only dependent variable in our case\n",
    "x = data.loc[:, ['T_left', 'T_bottom', 'Alpha','Timespacex1', 'Timespacey1', 'Timespacex2', 'Timespacey2', 'Timespacex3', 'Timespacey3']]\n",
    "y = data['Temperature']\n",
    "x.shape, y.shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}