3D 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\\Clean3d_master_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>T_back</th>\n",
       "      <th>T_front</th>\n",
       "      <th>x/L</th>\n",
       "      <th>y/L</th>\n",
       "      <th>z/L</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-1-2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
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       "      <td>100</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>Copper</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</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-1-2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006</td>\n",
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       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</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-1-2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</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-1-2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time (s) Material     Alpha      Part  Node     X      Y     Z  \\\n",
       "0      0.01   Copper  0.000111  PART-1-2     1  0.02  0.010  0.01   \n",
       "1      0.01   Copper  0.000111  PART-1-2     2  0.02  0.008  0.01   \n",
       "2      0.01   Copper  0.000111  PART-1-2     3  0.02  0.006  0.01   \n",
       "3      0.01   Copper  0.000111  PART-1-2     4  0.02  0.004  0.01   \n",
       "4      0.01   Copper  0.000111  PART-1-2     5  0.02  0.002  0.01   \n",
       "\n",
       "   Temperature  T_left  T_right  T_bottom  T_top  T_back  T_front  x/L  y/L  \\\n",
       "0        100.0     300      100       300    100     300      100  1.0  1.0   \n",
       "1        100.0     300      100       300    100     300      100  1.0  0.8   \n",
       "2        100.0     300      100       300    100     300      100  1.0  0.6   \n",
       "3        100.0     300      100       300    100     300      100  1.0  0.4   \n",
       "4        100.0     300      100       300    100     300      100  1.0  0.2   \n",
       "\n",
       "   z/L  \n",
       "0  1.0  \n",
       "1  1.0  \n",
       "2  1.0  \n",
       "3  1.0  \n",
       "4  1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>T_back</th>\n",
       "      <th>T_front</th>\n",
       "      <th>x/L</th>\n",
       "      <th>y/L</th>\n",
       "      <th>z/L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>5.690520e+05</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "      <td>569052.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.756263</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>2.077144e+02</td>\n",
       "      <td>450.034795</td>\n",
       "      <td>100.069589</td>\n",
       "      <td>400.069589</td>\n",
       "      <td>100.139179</td>\n",
       "      <td>333.263744</td>\n",
       "      <td>133.402923</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.763121</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>114.315089</td>\n",
       "      <td>0.006325</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>1.406171e+02</td>\n",
       "      <td>111.896817</td>\n",
       "      <td>81.607074</td>\n",
       "      <td>81.692304</td>\n",
       "      <td>81.649611</td>\n",
       "      <td>47.115832</td>\n",
       "      <td>47.165039</td>\n",
       "      <td>0.316228</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.341565</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>0.000000</td>\n",
       "      <td>-1.757920e-35</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>297.250000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time (s)          Alpha           Node              X  \\\n",
       "count  569052.000000  569052.000000  569052.000000  569052.000000   \n",
       "mean        0.756263       0.000050     198.500000       0.010000   \n",
       "std         1.763121       0.000041     114.315089       0.006325   \n",
       "min         0.010000       0.000004       1.000000       0.000000   \n",
       "25%         0.050000       0.000004      99.750000       0.004000   \n",
       "50%         0.120000       0.000064     198.500000       0.010000   \n",
       "75%         0.500000       0.000111     297.250000       0.016000   \n",
       "max        10.000000       0.000111     396.000000       0.020000   \n",
       "\n",
       "                   Y              Z   Temperature         T_left  \\\n",
       "count  569052.000000  569052.000000  5.690520e+05  569052.000000   \n",
       "mean        0.005000       0.005000  2.077144e+02     450.034795   \n",
       "std         0.003416       0.003416  1.406171e+02     111.896817   \n",
       "min         0.000000       0.000000 -1.757920e-35     300.000000   \n",
       "25%         0.002000       0.002000  1.000000e+02     300.000000   \n",
       "50%         0.005000       0.005000  2.000000e+02     500.000000   \n",
       "75%         0.008000       0.008000  3.000000e+02     600.000000   \n",
       "max         0.010000       0.010000  6.000000e+02     600.000000   \n",
       "\n",
       "             T_right       T_bottom          T_top         T_back  \\\n",
       "count  569052.000000  569052.000000  569052.000000  569052.000000   \n",
       "mean      100.069589     400.069589     100.139179     333.263744   \n",
       "std        81.607074      81.692304      81.649611      47.115832   \n",
       "min         0.000000     300.000000       0.000000     300.000000   \n",
       "25%         0.000000     300.000000       0.000000     300.000000   \n",
       "50%       100.000000     400.000000     100.000000     300.000000   \n",
       "75%       200.000000     500.000000     200.000000     400.000000   \n",
       "max       200.000000     500.000000     200.000000     400.000000   \n",
       "\n",
       "             T_front            x/L            y/L            z/L  \n",
       "count  569052.000000  569052.000000  569052.000000  569052.000000  \n",
       "mean      133.402923       0.500000       0.500000       0.500000  \n",
       "std        47.165039       0.316228       0.341565       0.341565  \n",
       "min       100.000000       0.000000       0.000000       0.000000  \n",
       "25%       100.000000       0.200000       0.200000       0.200000  \n",
       "50%       100.000000       0.500000       0.500000       0.500000  \n",
       "75%       200.000000       0.800000       0.800000       0.800000  \n",
       "max       200.000000       1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((569052, 11), (569052,))"
      ]
     },
     "execution_count": 7,
     "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', 'T_back', 'T_front', 'Time (s)', 'x/L', 'y/L', 'z/L', 'Alpha']]\n",
    "y = data['Temperature']\n",
    "x.shape, y.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43574948833666.38\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime of Linear Regression program (in seconds) is 9.110755920410156\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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error 83.72668375487768\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Absolute Error     84.20026556531944\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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Variables   Coefficient\n",
      "0     T_left  5.604197e-02\n",
      "1    T_right  6.699610e-02\n",
      "2   T_bottom -2.109277e+10\n",
      "3      T_top -1.593117e+11\n",
      "4     T_back -1.382189e+11\n",
      "5    T_front  2.014972e+11\n",
      "6   Time (s)  9.010306e+00\n",
      "7        x/L -1.057834e+02\n",
      "8        y/L -1.527585e+02\n",
      "9        z/L -1.613612e+02\n",
      "10     Alpha  3.227240e+05\n"
     ]
    }
   ],
   "source": [
    "#Regression Coefficients of the variables in order \n",
    "Coeff_data = {'Variables': ['T_left', 'T_right', 'T_bottom', 'T_top', 'T_back', 'T_front','Time (s)', 'x/L', 'y/L', 'z/L', \n",
    "                            'Alpha'], 'Coefficient': lr.coef_}\n",
    "df = pd.DataFrame(Coeff_data, columns = ['Variables', 'Coefficient'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficient plot')"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.48738963587848"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_x, train_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.17753413221803"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_x,test_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[134.09375   232.171875  240.796875  ... 237.609375  140.8828125\n",
      " 203.9375   ]\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(test_x)\n",
    "print (y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>306399</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>134.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114750</th>\n",
       "      <td>200.00000</td>\n",
       "      <td>232.171875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439305</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>240.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163614</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>142.539062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109257</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>405.453125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142312</th>\n",
       "      <td>200.00000</td>\n",
       "      <td>218.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434710</th>\n",
       "      <td>2.62269</td>\n",
       "      <td>188.734375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36817</th>\n",
       "      <td>400.00000</td>\n",
       "      <td>237.609375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512875</th>\n",
       "      <td>55.97630</td>\n",
       "      <td>140.882812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320819</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>203.937500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142263 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Actual   Predicted\n",
       "306399  100.00000  134.093750\n",
       "114750  200.00000  232.171875\n",
       "439305  300.00000  240.796875\n",
       "163614  300.00000  142.539062\n",
       "109257  300.00000  405.453125\n",
       "...           ...         ...\n",
       "142312  200.00000  218.656250\n",
       "434710    2.62269  188.734375\n",
       "36817   400.00000  237.609375\n",
       "512875   55.97630  140.882812\n",
       "320819  300.00000  203.937500\n",
       "\n",
       "[142263 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':test_y, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>y/L</th>\n",
       "      <th>z/L</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-1-2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.340833</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-1.224958</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>-0.423263</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>1.46385</td>\n",
       "      <td>1.467504</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-1-2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.340833</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-1.224958</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>-0.423263</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>0.87831</td>\n",
       "      <td>1.467504</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-1-2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.340833</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-1.224958</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>-0.423263</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>0.29277</td>\n",
       "      <td>1.467504</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-1-2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.340833</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-1.224958</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>-0.423263</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>-0.29277</td>\n",
       "      <td>1.467504</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-1-2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.340833</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-1.224958</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>-0.423263</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>-0.87831</td>\n",
       "      <td>1.467504</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>569047</th>\n",
       "      <td>10.00</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>392</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.340211</td>\n",
       "      <td>-1.226238</td>\n",
       "      <td>1.223255</td>\n",
       "      <td>1.223042</td>\n",
       "      <td>5.242831</td>\n",
       "      <td>-1.581139</td>\n",
       "      <td>0.87831</td>\n",
       "      <td>-1.136450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569048</th>\n",
       "      <td>10.00</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>393</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.340211</td>\n",
       "      <td>-1.226238</td>\n",
       "      <td>1.223255</td>\n",
       "      <td>1.223042</td>\n",
       "      <td>5.242831</td>\n",
       "      <td>-1.581139</td>\n",
       "      <td>0.29277</td>\n",
       "      <td>-1.136450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569049</th>\n",
       "      <td>10.00</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>394</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.340211</td>\n",
       "      <td>-1.226238</td>\n",
       "      <td>1.223255</td>\n",
       "      <td>1.223042</td>\n",
       "      <td>5.242831</td>\n",
       "      <td>-1.581139</td>\n",
       "      <td>-0.29277</td>\n",
       "      <td>-1.136450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569050</th>\n",
       "      <td>10.00</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>395</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.340211</td>\n",
       "      <td>-1.226238</td>\n",
       "      <td>1.223255</td>\n",
       "      <td>1.223042</td>\n",
       "      <td>5.242831</td>\n",
       "      <td>-1.581139</td>\n",
       "      <td>-0.87831</td>\n",
       "      <td>-1.136450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569051</th>\n",
       "      <td>10.00</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>396</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.340211</td>\n",
       "      <td>-1.226238</td>\n",
       "      <td>1.223255</td>\n",
       "      <td>1.223042</td>\n",
       "      <td>5.242831</td>\n",
       "      <td>-1.581139</td>\n",
       "      <td>-1.46385</td>\n",
       "      <td>-1.136450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569052 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time (s)             Material     Alpha      Part  Node     X      Y  \\\n",
       "0           0.01               Copper  0.000111  PART-1-2     1  0.02  0.010   \n",
       "1           0.01               Copper  0.000111  PART-1-2     2  0.02  0.008   \n",
       "2           0.01               Copper  0.000111  PART-1-2     3  0.02  0.006   \n",
       "3           0.01               Copper  0.000111  PART-1-2     4  0.02  0.004   \n",
       "4           0.01               Copper  0.000111  PART-1-2     5  0.02  0.002   \n",
       "...          ...                  ...       ...       ...   ...   ...    ...   \n",
       "569047     10.00  Stainless Steel 310  0.000004  PART-1-2   392  0.00  0.008   \n",
       "569048     10.00  Stainless Steel 310  0.000004  PART-1-2   393  0.00  0.006   \n",
       "569049     10.00  Stainless Steel 310  0.000004  PART-1-2   394  0.00  0.004   \n",
       "569050     10.00  Stainless Steel 310  0.000004  PART-1-2   395  0.00  0.002   \n",
       "569051     10.00  Stainless Steel 310  0.000004  PART-1-2   396  0.00  0.000   \n",
       "\n",
       "           Z  Temperature  T_left  ...  y/L  z/L  scaled_T_left  \\\n",
       "0       0.01        100.0     300  ...  1.0  1.0      -1.340833   \n",
       "1       0.01        100.0     300  ...  0.8  1.0      -1.340833   \n",
       "2       0.01        100.0     300  ...  0.6  1.0      -1.340833   \n",
       "3       0.01        100.0     300  ...  0.4  1.0      -1.340833   \n",
       "4       0.01        100.0     300  ...  0.2  1.0      -1.340833   \n",
       "...      ...          ...     ...  ...  ...  ...            ...   \n",
       "569047  0.00        300.0     600  ...  0.8  0.0       1.340211   \n",
       "569048  0.00        300.0     600  ...  0.6  0.0       1.340211   \n",
       "569049  0.00        300.0     600  ...  0.4  0.0       1.340211   \n",
       "569050  0.00        300.0     600  ...  0.2  0.0       1.340211   \n",
       "569051  0.00        300.0     600  ...  0.0  0.0       1.340211   \n",
       "\n",
       "        scaled_T_right  scaled_T_bottom  scaled_T_top  scaled_time  \\\n",
       "0            -0.000853        -1.224958     -0.001705    -0.423263   \n",
       "1            -0.000853        -1.224958     -0.001705    -0.423263   \n",
       "2            -0.000853        -1.224958     -0.001705    -0.423263   \n",
       "3            -0.000853        -1.224958     -0.001705    -0.423263   \n",
       "4            -0.000853        -1.224958     -0.001705    -0.423263   \n",
       "...                ...              ...           ...          ...   \n",
       "569047       -1.226238         1.223255      1.223042     5.242831   \n",
       "569048       -1.226238         1.223255      1.223042     5.242831   \n",
       "569049       -1.226238         1.223255      1.223042     5.242831   \n",
       "569050       -1.226238         1.223255      1.223042     5.242831   \n",
       "569051       -1.226238         1.223255      1.223042     5.242831   \n",
       "\n",
       "        scaled_location_x  scaled_location_y  scaled_alpha  \n",
       "0                1.581139            1.46385      1.467504  \n",
       "1                1.581139            0.87831      1.467504  \n",
       "2                1.581139            0.29277      1.467504  \n",
       "3                1.581139           -0.29277      1.467504  \n",
       "4                1.581139           -0.87831      1.467504  \n",
       "...                   ...                ...           ...  \n",
       "569047          -1.581139            0.87831     -1.136450  \n",
       "569048          -1.581139            0.29277     -1.136450  \n",
       "569049          -1.581139           -0.29277     -1.136450  \n",
       "569050          -1.581139           -0.87831     -1.136450  \n",
       "569051          -1.581139           -1.46385     -1.136450  \n",
       "\n",
       "[569052 rows x 26 columns]"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((569052, 8), (569052,))"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean MAE: 97.952\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": 26,
   "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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "207.68531326731053\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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime of Random Forest program (in seconds) is 2.057631015777588\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": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error 97.86241725573107\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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Absolute Error     98.19783212388971\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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.26310299,   5.45085794,  23.81821032,   6.81112921,\n",
       "        15.86830434, -33.41680853, -52.14182897,  13.28365372])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Variables  Coefficient\n",
      "0      scaled_T_left     6.263103\n",
      "1     scaled_T_right     5.450858\n",
      "2    scaled_T_bottom    23.818210\n",
      "3       scaled_T_top     6.811129\n",
      "4        scaled_time    15.868304\n",
      "5  scaled_location_x   -33.416809\n",
      "6  scaled_location_y   -52.141829\n",
      "7       scaled_Alpha    13.283654\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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficient plot')"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.10530864056646"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_x, train_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.002319487290904"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_x,test_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[214.59774159 215.98988257 160.01487098 ... 189.0100491  124.85889492\n",
      " 187.69993159]\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(test_x)\n",
    "print (y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>306399</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>214.597742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114750</th>\n",
       "      <td>200.00000</td>\n",
       "      <td>215.989883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439305</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>160.014871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163614</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>61.781702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109257</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>324.662793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142312</th>\n",
       "      <td>200.00000</td>\n",
       "      <td>299.277961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434710</th>\n",
       "      <td>2.62269</td>\n",
       "      <td>236.975384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36817</th>\n",
       "      <td>400.00000</td>\n",
       "      <td>189.010049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512875</th>\n",
       "      <td>55.97630</td>\n",
       "      <td>124.858895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320819</th>\n",
       "      <td>300.00000</td>\n",
       "      <td>187.699932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142263 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Actual   Predicted\n",
       "306399  100.00000  214.597742\n",
       "114750  200.00000  215.989883\n",
       "439305  300.00000  160.014871\n",
       "163614  300.00000   61.781702\n",
       "109257  300.00000  324.662793\n",
       "...           ...         ...\n",
       "142312  200.00000  299.277961\n",
       "434710    2.62269  236.975384\n",
       "36817   400.00000  189.010049\n",
       "512875   55.97630  124.858895\n",
       "320819  300.00000  187.699932\n",
       "\n",
       "[142263 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':test_y, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TMTPeeOONrMvefffdvPvuu1mXS+eZZ57hc5/73DbTPv74Y3r06MGaNdueOW/UqFE88MADea1LUlPAi7RS9913H0ceeSQTJ07MumyxAZ9Kp06dGDZsGA8++OCWaWvWrOG5555TgJeIAl6kFVq7di3PP/8848eP3y7gb7rpJvbff38GDBjA5ZdfzqRJk5g+fTpjx45l4MCBrF+/nvr6elas8Ofenj59OkOHDgXgxRdf5PDDD+fAAw/k8MMPZ968eRnLMWbMmG2ef/LkyQwfPpyOHTvmtK5rr72Wn/9860nF9ttvPxYuXAjAn//8Zw4++GAGDhzIOeecQ3NzsWfqrDzqJikSpYsvhlmzSrvOgQPh5pszLvLggw8yfPhw9tprL3bYYQdmzpzJoEGD+Mc//sGDDz7ItGnT6NixI6tWrWKHHXbgtttu4+c//zlDhgzJuN69996bZ599ltraWp544gmuvPJK/vrXv6Zdfvjw4XzjG99g5cqV9OjRg4kTJ3LBBRcUtK5Er7/+Ovfffz/PP/88bdu25bzzzmPChAmMGzcup8dXCwW8SCt03333cfHFFwMwevRo7rvvPgYNGsQTTzzBmWeeSceO/hziO+ywQ17rXbNmDWeccQZvvvkmZkZTU8YzytGuXTtGjBjBpEmTOOWUU5g1axbDhg0raF2JnnzySWbMmMFBBx0EwPr16+nVq1der6UaKOBFopSlpl0OK1eu5KmnnmL27NmYGc3NzZgZN910E865nLoM1tbWsnnzZoBt+o9fffXVHHvssUyePJmFCxduabrJZMyYMdxwww045xg5cuSWo0lzWVdiORLL4pzjjDPO4Mc//nHW569maoMXaWUmTZrEuHHjePvtt1m4cCGLFi2iX79+PPfccwwbNoy77rqLdevWAbBq1SoAunTpwkcffbRlHfX19cyYMQNgm2aTNWvW0KdPH8DvmM3Fsccey5tvvsntt9/OmDFj8lpXfX09M2fOBGDmzJksWLAAgOOPP55Jkybx/vvvb3kdb79d0SP/FkQBH1cvvQSrV0ddCqlC9913H1/4whe2mXbKKadw7733Mnz4cEaMGMGQIUMYOHDglh2YX/va1zj33HO37GT9wQ9+wEUXXcRRRx1FmzZttqzne9/7HldccQVHHHFEzjs1a2pqOOWUU1i5ciVHH310Xus65ZRTWLVqFQMHDuQ3v/kNe+21FwD9+/fnhhtuYNiwYRxwwAGccMIJvPdexZ1fu2hVcU7WIUOGuKo64cfHH0Pnzv6SUGuS6vD666+zzz77RF0MSZLqczGzGc65zHuWY0w1+DgKe1WsXQvBX2QRkXwp4OPo5Ze33m5oiK4cIlLRFPBx9M47W2+/+WZ05RCRiqaAj6Nly6BncCL3VrjnX0RKQ/3g42jZMth1V1i/HoLDwUVE8lVUDd7MhpvZPDNrMLPLU8xvb2b3B/OnmVl9MP0EM5thZq8F18clPOaZYJ2zgkvrO/xs2TLYaSfYcUdYvjzq0ohIhSo44M2sDXA7cBLQHxhjZv2TFvs68IFzbg/gV8BPg+krgM875/YHzgD+lPS4sc65gcHl/ULLWLESA141eCmDNm3aMHDgwC2XhQsXMn36dC688ELAD8k7derULcs/+OCDzJ07N+/n6dy583bThg4dymOPPbbNtJtvvpnzzjsv73VJZsXU4A8GGpxzbznnGoGJwMikZUYC9wS3JwHHm5k55152zoVjj84B6sysfRFlqS4rV/pw79lTAS9l0aFDB2bNmrXlUl9fz5AhQ7j11luB0gV8KskjSAJMnDhxm6NYpTSKCfg+wKKE+4uDaSmXcc5tAtYAPZKWOQV42Tm3MWHaH4LmmastzcAYZna2mU03s+nLq6kZY+NGaGyEbt3URCMtKjyRxsKFC7njjjv41a9+xcCBA/n3v//NlClTuPTSSxk4cCDz589n/vz5DB8+nMGDB3PUUUdtOWnIggULOOywwzjooIO4+uqrUz7PqaeeysMPP8zGjf4rv3DhQt59912OPPJI1q5dy/HHH8+gQYPYf//9eeihh9KWM3T++edvGcpgxowZHHPMMQwePJgTTzxxy9Grt956K/379+eAAw5g9OjRpXzbYq2Ynaypgjf5sNiMy5jZvvhmm2EJ88c655aYWRfgr8BXgT9utxLn7gTuBH8ka35Fj7Hw7DbdukGPHr42L1UrotGCWb9+PQMHDgSgX79+TJ48ecu8+vp6zj33XDp37sx3v/tdAEaMGMHnPvc5Tj31VMCP9XLHHXew5557Mm3aNM477zyeeuopLrroIr71rW8xbtw4br/99pTP3aNHDw4++GD++c9/MnLkSCZOnMjpp5+OmVFXV8fkyZPp2rUrK1as4NBDD2XEiBE5DYDW1NTEBRdcwEMPPUTPnj25//77ueqqq7jrrrv4yU9+woIFC2jfvj2rW9EQIMUE/GJgl4T7fYHkU76Eyyw2s1qgG7AKwMz6ApOBcc65+eEDnHNLguuPzOxefFPQdgFftcKA79rVh/zatdDcDAnjfYgUK2yiKcTatWuZOnUqp5122pZpYW38+eef3zL42Fe/+lUuu+yylOsIm2nCgL/rrrsAPwrklVdeybPPPktNTQ1Llixh2bJlfPKTn8xarnnz5jF79mxOOOEEAJqbm9l5550BOOCAAxg7diyjRo1i1KhRBb3uSlRMwL8E7Glm/YAlwGjgy0nLTMHvRH0BOBV4yjnnzKw78AhwhXPu+XDh4Eegu3NuhZm1BT4HPFFEGStPYg2+a1d/e+1af1+qTgSjBRdt8+bNdO/ePe0PRC617VGjRnHJJZcwc+ZM1q9fz6BBgwCYMGECy5cvZ8aMGbRt25b6+vpthiOGzEME77vvvrzwwgvbPd8jjzzCs88+y5QpU7j++uuZM2cOtbXV30u84Db4oE39fOAx4HXgAefcHDO7zsxGBIuNB3qYWQNwCRB2pTwf2AO4Oqk7ZHvgMTN7FZiF/+H4XaFlrEiJAR+G+ocfRlceaZWShwdOvN+1a1f69evHX/7yF8AH6yuvvALAEUccsWUH6oQJE9Kuv3PnzgwdOpSzzjpruyGCe/XqRdu2bXn66adTDvG76667MnfuXDZu3MiaNWt48sknAfj0pz/N8uXLtwR8U1MTc+bMYfPmzSxatIhjjz2Wm266idWrV7N27dpi3p6KUVQ/eOfco865vZxzuzvnfhRMu8Y5NyW4vcE5d5pzbg/n3MHOubeC6Tc45zoldIUc6Jx73zn3sXNusHPuAOfcvs65i5xzretEimGYJ9bgk846L1Jun//855k8eTIDBw7kP//5D6NHj+ZnP/sZBx54IPPnz2fChAmMHz+eAQMGsO+++27ZGXrLLbdw++23c9BBB7Emy3Y7ZswYXnnllW12eo4dO5bp06czZMgQJkyYwN57773d43bZZRe+9KUvbWl2OfDAAwF/dqhJkyZx2WWXMWDAAAYOHMjUqVNpbm7mK1/5Cvvvvz8HHngg3/nOd+jevXsJ36340nDBcXP33XDmmTB/vh+HZvhweP55OPzwqEsmJaLhguNJwwVL+YV/i7t02dpEoxq8iBRAAR83wanS6NRpaxON2uBFpAAK+LgJA76uTjX4KlYNTaPVpFo/DwV83KxbBx06QE2Nr8WH06Rq1NXVsXLlyqoNlUrjnGPlypXU1dVFXZSSq/6OoJVm3Tro2NHfDq8//ji68kjJ9e3bl8WLF1NVQ2xUuLq6Ovr27Rt1MUpOAR83iQHfrh3U1qoGX2Xatm1Lv379oi6GtAJqoombxIAHf1s1eBEpgAI+bpIDvlMn1eBFpCAK+LhRDV5ESkQBHzeqwYtIiSjg40Y1eBEpEQV83KSqwSvgRaQACvi4SVWDVxONiBRAAR83qsGLSIko4ONGNXgRKREFfJw0N0Njo2rwIlISCvg4Wb/eX6sGLyIloICPkzDIk2vwjY2waVM0ZRKRiqWAj5NUAR/eVi1eRPKkgI+TdDV4UDu8iORNAR8nqsGLSAkp4ONENXgRKSEFfJyoBi8iJaSAjxPV4EWkhBTwcRIGfIcOW6fpvKwiUiAFfJxs2OCvE8/urhq8iBRIAR8nGzf66/btt04La/PhPBGRHCng4yRVwIe1+XAYAxGRHCng4yRVE014O5wnIpIjBXychDX4du22TgubaBTwIpInBXycbNzow91s67Qw7BXwIpInBXycbNy4bfs7+LCvq1MbvIjkTQEfJxs2bB/w4ANeNXgRyZMCPk42btx2B2uoQwcFvIjkTQEfJ6maaEA1eBEpiAI+ThTwIlJCCvg4ydQGr52sIpInBXycqA1eREpIAR8naqIRkRJSwMeJAl5ESkgBHyfqBy8iJaSAj5N0bfDaySoiBVDAx0m6JhrtZBWRAijg40Rt8CJSQgr4OFHAi0gJKeDjZMOG9G3wCngRyZMCPk4ytcE3N0NTU8uXSUQqlgI+Lpqb/SVdEw2oFi8ieVHAx0WqE26HFPAiUgAFfFykOuF2SAEvIgVQwMdFphq8TrwtIgVQwMdFLk00OppVRPKggI8LtcGLSIkp4ONCbfAiUmIK+LhQG7yIlJgCPi4yBXw4TQEvInlQwMdFLgEfLiMikgMFfFyEtXMFvIiUiAI+Lhob/bUCXkRKRAEfF2HAt2u3/TwFvIgUQAEfF7kEvHayikgeigp4MxtuZvPMrMHMLk8xv72Z3R/Mn2Zm9cH0E8xshpm9Flwfl/CYwcH0BjO71cysmDJWjHAoYNXgRaRECg54M2sD3A6cBPQHxphZ/6TFvg584JzbA/gV8NNg+grg8865/YEzgD8lPOY3wNnAnsFleKFlrCiZavDhNAW8iOShmBr8wUCDc+4t51wjMBEYmbTMSOCe4PYk4HgzM+fcy865d4Ppc4C6oLa/M9DVOfeCc84BfwRGFVHGypEp4GtqoG1bBbyI5KWYgO8DLEq4vziYlnIZ59wmYA3QI2mZU4CXnXMbg+UXZ1knAGZ2tplNN7Ppy5cvL/hFxEamgAffTKOAF5E8FBPwqdrGXT7LmNm++Gabc/JYp5/o3J3OuSHOuSE9e/bMobgxp4AXkRIrJuAXA7sk3O8LvJtuGTOrBboBq4L7fYHJwDjn3PyE5ftmWWd1CgO+tjb1fAW8iOSpmIB/CdjTzPqZWTtgNDAlaZkp+J2oAKcCTznnnJl1Bx4BrnDOPR8u7Jx7D/jIzA4Nes+MAx4qooyVo7HRt7On6zSkgBeRPBUc8EGb+vnAY8DrwAPOuTlmdp2ZjQgWGw/0MLMG4BIg7Ep5PrAHcLWZzQouvYJ53wJ+DzQA84F/FFrGitLUlL55BhTwIpK3NO0BuXHOPQo8mjTtmoTbG4DTUjzuBuCGNOucDuxXTLkqUmOjAl5ESkpHssaFAl5ESkwBHxcKeBEpMQV8XGQL+Lo6BbyI5EUBHxeqwYtIiSng4yLsJpmOAl5E8qSAjwt1kxSRElPAx4WaaESkxBTwcaGAF5ESU8DHhQJeREpMAR8XCngRKTEFfFzkGvAu5ejJIiLbUcDHRS7dJJ2DTZtarkwiUtEU8HGRSzdJUDONiORMAR8XuTTRgAJeRHKmgI8LBbyIlJgCPi4U8CJSYgr4uFDAi0iJKeDjQgEvIiWmgI+D5mZ/UcCLSAkp4OOgqclfZ+sHDwp4EcmZAj4OwoBXDV5ESkgBHweNjf5aAS8iJaSAjwMFvIiUgQI+DhTwIlIGCvg4UMCLSBko4ONAAS8iZaCAjwMFvIiUgQI+DtQPXkTKQAEfB6rBi0gZKODjIJeAr62FmhoFvIjkTAEfB7kEPOjE2yKSFwV8HCjgRaQMFPBxoIAXkTJQwMeBAl5EykABHwe5jCYJPuA3bCh/eUSkKijg4yCswWfqBw+qwYtIXhTwcaAmGhEpAwV8HCjgRaQMFPBxoIAXkTJQwMeBAl5EykABHweNjX4YgjZtMi+ngBeRPCjg46CxMXvtHRTwIpIXBXwcNDUp4EWk5BTwcdDYmL0PPCjgRSQvCvg4UBONiJSBAj4Ocg34ujoFvIjkTAEfB6rBi0gZKODjIJ+A37QJNm8uf5lEpOIp4OMgn4AH1eJFJCcK+DjIp5skKOBFJCcK+DhQDV5EykABHwf59IMHBbyI5EQBHweqwYtIGekwkOIAACAASURBVCjg40ABLyJloICPAwW8iJSBAj4OFPAiUgYK+DhQN0kRKQMFfByoBi8iZaCAjwMFvIiUgQI+DtQPXkTKQAEfB/nW4DdsKG95RKQqKOCj5pyaaESkLIoKeDMbbmbzzKzBzC5PMb+9md0fzJ9mZvXB9B5m9rSZrTWz25Ie80ywzlnBpVcxZYy95mYf8rme8AMU8CKSk9pCH2hmbYDbgROAxcBLZjbFOTc3YbGvAx845/Yws9HAT4HTgQ3A1cB+wSXZWOfc9ELLVlEaG/21avAiUmLF1OAPBhqcc2855xqBicDIpGVGAvcEtycBx5uZOec+ds49hw/61q2pyV8r4EWkxIoJ+D7AooT7i4NpKZdxzm0C1gA9clj3H4LmmavNzFItYGZnm9l0M5u+fPny/EsfF/nU4MOeNgp4EclBMQGfKnhdAcskG+uc2x84Krh8NdVCzrk7nXNDnHNDevbsmbWwsZVPwJvpvKwikrNiAn4xsEvC/b7Au+mWMbNaoBuwKtNKnXNLguuPgHvxTUHVKwz4XPrBgwJeRHJWTMC/BOxpZv3MrB0wGpiStMwU4Izg9qnAU865tDV4M6s1sx2D222BzwGziyhj/OVTgwcFvIjkrOBeNM65TWZ2PvAY0Aa4yzk3x8yuA6Y756YA44E/mVkDvuY+Ony8mS0EugLtzGwUMAx4G3gsCPc2wBPA7wotY0UoJOB1oJOI5KDggAdwzj0KPJo07ZqE2xuA09I8tj7NagcXU6aKoxq8iJSJjmSNWj7dJEEBLyI5U8BHTTV4ESkTBXzU8g34ujoFvIjkRAEfNXWTFJEyUcBHTU00IlImCvioKeBFpEwU8FFTwItImSjgo1ZIN0kd6CQiOVDAR001eBEpEwV81BTwIlImCvioKeBFpEwU8FFTP3gRKRMFfNTyDfi6Or9jdvPm8pVJRKqCAj5qjY1QWws1OX4U4XlZwx8GEZE0FPBRa2rKvf0ddOJtEcmZAj5qjY0KeBEpCwV81BTwIlImCvioFRrwOppVRLJQwEdNNXgRKRMFfNQaG3PvIgkKeBHJmQI+agp4ESkTBXzUGhu3hnYuFPAikiMFfNQU8CJSJgr4qOW7k7Wuzl8r4EUkCwV81NSLRkTKRAEfNQW8iJSJAj5qGzfqQCcRKQsFfNRUgxeRMlHAR00BLyJlooCPmrpJikiZKOCjlm8NPlxWAS8iWSjgo5ZvwJv55RXwIpKFAj5q+QY86MTbIpITBXyUnMu/myT4o1kV8CKShQI+Ss3NPuRVgxeRMlDAR6mx0V8XEvA60ElEslDAR6mYgFcNXkSyUMBHKQz4fPrBh8sr4EUkCwV8lFSDF5EyUsBHSQEvImWkgI9SGNIKeBEpAwV8lFSDF5EyUsBHSQEvImWkgI+SAl5EykgBH6VCA15DFYhIDhTwUSqmH7yOZBWRLBTwUVIvGhEpIwV8lNQGLyJlpICPUjEB39QEmzeXvkwiUjUU8FEqJuATHy8ikoICPkrFBryaaUQkAwV8lBTwIlJGCvgoKeBFpIwU8FEKA7qQfvCJjxcRSUEBH6WwBt+2bX6Pq6vz1zrYSUQyUMBHqbERamuhJs+PQTV4EcmBAj5KjY35t7+DAl5EcqKAj5ICXkTKSAEfJQW8iJSRAj5KCngRKSMFfJQ2bsy/iyQo4EUkJ0UFvJkNN7N5ZtZgZpenmN/ezO4P5k8zs/pgeg8ze9rM1prZbUmPGWxmrwWPudXMrJgyxppq8CJSRgUHvJm1AW4HTgL6A2PMrH/SYl8HPnDO7QH8CvhpMH0DcDXw3RSr/g1wNrBncBleaBljTwEvImVUTA3+YKDBOfeWc64RmAiMTFpmJHBPcHsScLyZmXPuY+fcc/ig38LMdga6OudecM454I/AqCLKGG/FBrwOdBKRDIoJ+D7AooT7i4NpKZdxzm0C1gA9sqxzcZZ1AmBmZ5vZdDObvnz58jyLHhOqwYtIGRUT8Knaxl0ByxS0vHPuTufcEOfckJ49e2ZYZYwVGvDhUAUKeBHJoJiAXwzsknC/L/BuumXMrBboBqzKss6+WdZZPQoN+PAxCngRyaCYgH8J2NPM+plZO2A0MCVpmSnAGcHtU4Gngrb1lJxz7wEfmdmhQe+ZccBDRZQx3jZuLCzgzfzjFPAikkFtoQ90zm0ys/OBx4A2wF3OuTlmdh0w3Tk3BRgP/MnMGvA199Hh481sIdAVaGdmo4Bhzrm5wLeAu4EOwD+CS3VqbCysHzzoxNsiklXBAQ/gnHsUeDRp2jUJtzcAp6V5bH2a6dOB/YopV8UotIkGfDu8etGISAY6kjVKxQR8hw6wfn1pyyMiVUUBHyXV4EWkjBTwUVINXkTKSAEfJdXgRaSMFPBRKrSbJPgavAJeRDJQwEfFOWhqKq4GryYaEclAAR+VpiZ/XWg/eNXgRSQLBXxUGhv9tWrwIlImCvioFBvwqsGLSBYK+KiEwwy0bVvY41WDF5EsFPBRCQM+HPo3X6rBi0gWCviohAFf6E7WsAaffnBOEWnlFPBRCWvfxdTgN2+GTZtKVyYRqSoK+KiUogYPaocXkbQU8FEpNuA7dPDXaocXkTQU8FEptolGNXgRyUIBHxXV4EWkzBTwUVENXkTKTAEfFdXgRaTMFPBRKVUvGgW8iKShgI9KKfrBg5poRCQtBXxUVIMXkTJTwEelVG3wqsGLSBoK+KiENW/V4EWkTBTwUdm40Q8VXFPgR6AavIhkoYCPyoYNhe9gBdXgRSQrBXxUNm4svHkGdKCTiGSlgI9KsQHfti20aaMavIikpYCPSrFNNKDT9olIRgr4qBRbgwedtk9EMlLAR6UUAa8avIhkoICPSimaaFSDF5EMFPBRUQ1eRMpMAR8V1eBFpMwU8FEpVQ1eAS8iaSjgo1KqXjRqohGRNBTwUSlVP3jV4EUkDQV8VFSDF5EyU8BHRW3wIlJmCviolKoXjWrwIpKGAj4KzqkGLyJlp4CPwqZNsHlzaWrw69aVpkwiUnUU8FEo9nysoY4dobkZmpqKL5OIVJ3aqAtQkZYsgXfegU6doH9/qM3zbQzbzcPT7hWqY0d/vW4ddOtW3LpEpOqoBp+PtWvhs5+Fvn3h8MNhwADYYw+YNSu/9YTNKsUGfKdO/vrjj4tbj4hUJQV8Pi68EB57DH74Q3jkEfjTn3xb+qhR+YVsWIMPa+CFSqzBi4gkURNNrt56C+6+Gy65BK65Zuv0Pn3guOPgnnvgvPNyW1c5mmhERJKoBp+rP/3JX1988bbThw6FgQPhj3/MfV0KeBFpAQr4XD38MBx2mG9/T2QGp5wC06bB0qW5ratUbfBhwKsNXkRSUMDnYu1amDEDTjgh9fxhw/z1f/6T2/pK1QYf7mRVDV5EUlDA52LWLH/06ZAhqecfeKAP6+eey219aqIRkRaggM/F9On+evDg1PPbtvVdJl99Nbf1KeBFpAUo4HMxYwb07g0775x+mX33hdmzc1tfqQNebfAikoICPhczZqSvvYf22w9WrID338++vlIf6KQavIikoIDPZtMmaGjwNfRMwvm51OJLtZM1HKxMAS8iKSjgs1m0yA/mtfvumZcLA37OnOzrXL8eamp8230xamr8vwA10YhICgr4bObP99d77JF5uU9+Erp3h9dfz77O9et9MJsVX76OHVWDF5GUFPDZNDT462wBbwa77QYLF2Zf57p1xbe/hzp1UsCLSEoK+Gzmz/fjtvfunX3Z+vrcAn79+uLb30OqwYtIGgr4bBoafPt7TQ5vVRjwzmVeLmyiKYWOHdUGLyIpaTTJbN56K/sO1lB9vQ/v5cuhV6/0y5U64FWDz8uiRf6g41mzfK/Wjz/2H0ePHrDXXnDIIX78uFLsIhGJkgI+m0WL4Kijclu2vt5fL1iQOeBL3Qa/enVp1lXFNm+GBx6AX/8apk7109q18x9T585bf5fD38pdd/UDh37rW8WfWVEkKkU10ZjZcDObZ2YNZnZ5ivntzez+YP40M6tPmHdFMH2emZ2YMH2hmb1mZrPMbHox5Svaxx/DBx9sP4JkOmHAZ2uHL3UbvJpoMvr3v+HQQ2HMGFi1Cm68EV5+2Y8ht2iR7/i0cKG//847MH6831/+ne/A3nvnPoacSNwUHPBm1ga4HTgJ6A+MMbP+SYt9HfjAObcH8Cvgp8Fj+wOjgX2B4cD/C9YXOtY5N9A5l2Z0rxayZIm/zjXgd93VXy9YkHk5NdG0iNdfhxEj/JD9773nz8kyezZccYVvgkk+DMEMdtkFzjoLnnoKHn/cn2536FC44Qb/L0CkkhRTgz8YaHDOveWcawQmAiOTlhkJ3BPcngQcb2YWTJ/onNvonFsANATri5fFi/11rgHftas/+XX4w5COAr6sli6Fc8+F/ff3tfcf/xj+9z8YNw7atMn++NBnPgMzZ/qa/9VXw8iRag2TylJMwPcBFiXcXxxMS7mMc24TsAbokeWxDviXmc0ws7PTPbmZnW1m081s+vLly4t4GRmEAd8n+WVl0Ls3vPtu5mVK3Qa/dm1p1lXh1q71p8vdYw/fzPLtb/tOUJdfXvjb3aWLP5nXbbfBP/8JBx+c+5hyIlErJuBT9TFI7h+YbplMjz3COTcI3/TzbTM7OtWTO+fudM4Ncc4N6dmzZ65lzk8hAd+nT/aAL2UbfJcuvg0+W9fMKrZpE/zud7DnnnDttXDSSb555pZboBSbhpn/sXjmGfjoI9+e/8ADxa9XpNyKCfjFwC4J9/sCycm2ZRkzqwW6AasyPdY5F16/D0wmyqabJUtghx3yC+NcavClbKLp3NmHeytspnHOn0lxwAA4+2y/Y3TqVPjLX7IfeFyII47wA4sOGACnnw7f+57/cRGJq2IC/iVgTzPrZ2bt8DtNpyQtMwU4I7h9KvCUc84F00cHvWz6AXsCL5pZJzPrAmBmnYBhQHR/iBcvzr39PdS7t9+jl26PnHOlD3jwVctWZPp0OO44+Pzn/Vhwf/ub79t+2GHlfd7eveHpp+G88+BnP4OTT/Y9c0TiqOCAD9rUzwceA14HHnDOzTGz68xsRLDYeKCHmTUAlwCXB4+dAzwAzAX+CXzbOdcM7AQ8Z2avAC8Cjzjn/lloGYu2ZEluQxQk6t3bJ87Klannb9gAzc1bg7lYXbr461bSDr9wIXz5y3DQQX7gzttu89df+ELLHZjUrh3cfrtv5//3v31ZXnutZZ5bJB9FHejknHsUeDRp2jUJtzcAp6V57I+AHyVNewsYUEyZSmrpUjjggPweE/4gvPtu6gbgMIjDYC5WK6nBNzfDL38J3/++7wlz1VW+iaRr1+jKdNZZ0L8/fPGLvl3+3nt9TxuRuNBYNOls3uyPY//kJ/N7XGLApxIGcakCvhXU4FevhhNP9IF+8sm+y+MNN0Qb7qFDD/Xt8vvt54P+7rujLpHIVgr4dD74wDe17LRTfo/LNeBL1UQTrqdKA/7dd+Hoo+HZZ+H3v/dt7fnuFim3nXeGJ5+E44+HM8+Em2+OukQinsaiSWfpUn+dbw0+XD5dwJe6iSZcTxU20axe7UNz8WJ49FF/4FFcde4Mf/87jB3rhzhYtcr3ydeAZRIlBXw6y5b563wDvn172HHHlmuiqdIafFMTnHaaH47/8cfhmGOiLlF27dvDxIlwzjlw/fW+le/66xXyEh0FfDphDT7fJhrI3Be+XG3wVVSDdw4uuACeeALuuqsywj1UW+sPuqqpgR/9yI9384MfRF0qaa0U8OkU2kQDuQW82uDTuvlm+O1v4bLLfJt2pamp8eXftMkfWdu2LVx5ZdSlktZIAZ/OsmW+w3O3bvk/tndveOWV1PNK3Qbfrp2/VEkN/u9/h//7P98j5cYboy5N4Wpq/E7h5mbfpbO21vcCEmlJCvh0li71tfdCGlB79/Y/EM3N2w9fWOomGvC1+Cqowc+a5UduHDQI/vjH3M6SGGdt2sAf/uD3J1x2ma/Jf+c7UZdKWhMFfDrLlhXWPAM+4MN+9DvvvO28jz7aWusulS5dKr4G/957ftiB7t1hyhQ/SGY1aNPGj0bZ3AyXXOJr8hdcEHWppLVQwKezdCl86lOFPTaxL3xywK9dW7r291CF1+DXrfMn5li1yo8nk+/oEHFXWwsTJvg2+Qsv9DX5c8+NulTSGlT4n+AyCptoCpHpYKePPipt8wz49VVowDsHX/uaPxr0vvvgwAOjLlF5tG3ru1B+/vP+PK+//33UJZLWQAGfSnOzPwNzoQEfjh+f6sxO5Qj4zp0rtonmxhv98L4/+YmvxVezdu38az35ZD+88T33ZH+MSDEU8KmsWOHb0AvpAw/Qq5ffQ6gafEZTpvjBw8aOhUsvjbo0LaN9e/jrX/0Rul//uj+IS6RcFPCpFNMHHnyj6047pQ74crXBV1gNfu5c+MpXYPBgf2BQazras67Oh3z//nDqqf69ECkHBXwq4TAFhdbgIf3BTuWowXfvDmvWlHadZbRmjR9Wt2NHePDB0p37pJJ07QqPPOJf+xe/WJF/wKQCKOBTKbYGDy0b8N26+dRMdxapGHHOtz8vWOBrsXEbGbIl7bKL3/H65pt+/JpWfFpdKRMFfCqFDjSWKFPAl7qJpnt3H+4VUA0cP96fsPr66/05Tlu7oUPhuuv8yULuvDPq0ki1UcCnsnSpbz8oJoh79/Y9cRobt07bvNnXtD/xieLLmKh7d3+9enVp11tiDQ1w0UX+XKqXXRZ1aeLjiiv8CU0uvBBmzoy6NFJNFPCpLF1aXPs7bO0LHzb3gK+9b94MO+xQ3LqThQEf43b45mbf371tW989sNKHISilmhr485/9GR6/9CX48MOoSyTVQl+zVJYu3f4I1HylOthp1Sp/XeoafDggWoxr8L/4BTz/vD9Jdmtud09nxx19e/yCBRrKQEpHAZ9KMUexhlIF/Acf+Oty1eBjGvCzZ8PVV/veImPHRl2a+DrySP8+/fGP/qhekWIp4FOptBp8jAO+sRHGjfN/Mu64o3X1dy/E978Phx/ux6pZuDDq0kilU8An27jRB3GxNfgdd/QNzi1Rg49xE81118HLL/seIj17Rl2a+Kut9e3x4P/tbNoUbXmksingk5WiiyT4PWc777xtwK9c6a9bSRv8s8/6sWbOOgtGjYq6NJWjXz//b2fqVLjhhqhLI5VMAZ8s7PVSbBMN+GaaxAHH3n/fX5e6Ktuune/WGaNeNKtXw1e/CrvvDrfcEnVpKs+YMb5p6/rr/RDKIoVQwCd77z1/XWwNHvyokosXb72/bJlvnmnbtvh1J+vePVY1+G9/2/+2/fnPpT+uq7W47Taor/dNNTH6aKWCKOCTlbIGv9tu8NZbvhM4+IAvtn99Ot26xSYFJkzwR2Zeey0cckjUpalcXbr493HJEv+DKZIvBXyypUt9V49SNKPssYfvRhI205Qz4GNSg29o8Ce0OOIIf4SmFOeQQ+Caa3zQ33tv1KWRSqOAT/bee1t7wBRrjz38dUODv162zI8VXw4xCPiNG+H0031PkHvv3f5841KYK6/0XSfPOw/efjvq0kglUcAnK0Uf+FBiwDvn2+PLdRjnJz6xtZ99RC691I+lcvfdhZ/OVrZXW+tP3L15s9/xGrb4iWSjgE9WiqNYQ337+lP4NDT4HjTr1/s+cOXQs6cf3Cwif/sb/PrXcPHF1X/qvSjstpt/f599Fn72s6hLI5VCAZ/svfdKF/A1Nf6b2dDgBxkB3y2iHHr29MMFb9hQnvVnsGCB7+s+ZAj89Kct/vStxrhxcNppfjgDjTopuVDAJ3KutE00APvsA6+9BvPn+/vlrMFDi9fiGxth9Gj/1t1/v++SL+Vh5g+A2mkn+PKXYd26qEskcaeAT7R6tU+sUvZ0OeggX4N/4gl/Ms699irduhOFAb9iRXnWn8aVV8KLL/oTeey2W4s+dau0ww5+MLL//c/vdNVZoCQTBXyi8KCkUu4IPewwf3333TBwYHkOcgLf8wdatAb/wAN+GODzzvMnj5aWcdxxvuvkPffoLFCSmQI+0aJF/rqUAX/kkf4MywCf/Wzp1pushZtoZs3yJ/A4/HD45S9b5CklwTXXwEkn+bHjp02LujQSVwr4RGENfpddSrfONm3gkUf80T8XXli69SZrwYB//30YORJ69PAnzm7fvuxPKUnCs0D16eP/PUXYgUpiTAGfaNEi/80pVS+a0JFHwv/7f1tr8uXwiU/4H5Myf9MbG32gvP8+PPhg6d8qyd0OO/gf2OXL/QFmTU1Rl0jiRgGfaNEiPwJkbW3UJclfTY2vUpdxJ6tzcM458J//wF13weDBZXsqydGgQb4d/umnfXONdrpKogpMsjIq55GmLWHHHctag//e9/y+4muv9cPZSjyMGwdz5/pjEPbZBy66KOoSSVyoBp9o0aLStr+3tJ49t445X2I33QQ//zmcf77fwSfxcuON/qQql1wCjz4adWkkLhTwIed8wFdyDb5Pn23PIFUi48fDZZf5Wvstt+i8qnEU7nQdMMAfePbKK1GXSOJAAR9ascKPFVPJNfhddvHNTJs3l2yVkyfD2WfD8OG+eaZGW0xsdeoEf/+7PzXA8OFbD56W1ktf11D4bdh992jLUYy+fX1XihI10zz9tK8NHnIITJqkYQgqQZ8+8K9/+c1g2LCtJyiT1kkBHwoDPhzitxKF/z4STxNYoBkzfF/3PfeEhx/2tUOpDPvs49vhly2DE0+EDz6IukQSFQV8qNyDgbWEMODDI3ILNG+eP0pyhx3gscf8tVSWgw/2xym88QacfHKszscuLUgBH5o/3/+/7dAh6pIULtxBXEQNvqHBj3ViBo8/7t8SqUyf+Ywf4XP6dN9cE4MzOkoLU8CH5s+v7PZ38N0k27cvuAb/1ltw7LH+aNUnn/TNM1LZvvAFv//k5Zd94Ed80i9pYQr4UDUEvJmvxRdQg1+40If7unV+ZOP99it98SQaI0f63lCvvQbHH69xa1oTBTz4BsqlS8s3VntLqq/Pu3/cm2/C0KHw4Yc+3AcMKEvJJEKf/SxMmeL3rxx22NbzwEt1U8CDr9oAHHBAtOUohb33htdfz3lQkpkz4Ygj4OOPfbgfeGCZyyeROfFEeOopX5857DD473+jLpGUmwIe4NVX/XU1BPw++8BHH+V0ROvTT/uae4cO8NxzGjysNTj0UJg61R8MddxxvulGqpcCHnzAd+9eHV1G+vf316+/nnGxv/3NH+34qU/5L/ynP90CZZNY2HNP/5kfcAB88Yv+JN7NzVGXSspBAQ8+4A84oDoGWdlnH3+dIeB/9zs47TRfY3/22er4XZP89OoFzzwDZ50FN9zg2+hXroy6VFJqCvimJh/w1bJncaed/L+RuXO3m+WcHwny7LN9e+wTT+ggptasrs4PJBeOJx/+4Ev1UMDPnOn3MB51VNQlKQ0zf3LvF1/cZvLGjfDVr8L11/ta20MPQceOEZVRYuWb3/QncampgWOOgXPP1ZGv1UIB/8wz/vqYYyItRkkdfbQ/K3bwLV21yh/JOGGC/zv++99D27YRl1Fi5eCDfWeySy7xTXj9+/vTAeoMUZVNAf/MM35r7tUr6pKUztFH+yGDp07lpZdgyBDfJe7ee+Gqq6pjV4OUXqdO8ItfwAsv+LM/nnqqP53w889HXTIpVOsO+DVrfMAff3zUJSmtww6jqU0dN/7Y93FvbvYvU6fZk1wcfLBvubzzTliwwIf8qFF+hFGpLK074B94ADZs8I3TVeTJFzoyuMNcrvrPSYwauZmXX/YHtojkqrbWt82/+aZv1nv6af9PcNgwf7CUmm4qQ+sNeOe2NjYOGRJ1aYrmnD/Rwwkn+EGlPuzQiwcZyQNn/lM9ZaRgnTr5Zr133oGf/MR3ODv+eL8f/5e/9GPOS3y13oC/91546SV/CvoKbpRet86fSm/AAN/1cfZs3446b35bRu78Evzwh74rqEgRunXz5+VduBB++1s/aOn//Z8/huKzn/XNOUuWRF1K2Y5zruALMByYBzQAl6eY3x64P5g/DahPmHdFMH0ecGKu60x1GTx4sMvZO+84d+mlzrVv79zhhzvX1JT7Y2Ng82bn3nrLufHjnRszxrnOnZ0D5/bf37k//MG5DRsSFr7/fj/zuOOce/75qIosVWruXOcuv9y5XXf1mxk4N2CAc5dc4tx99zk3f77fXisZMN0VkZFRX8wV2JhmZm2A/wEnAIuBl4Axzrm5CcucBxzgnDvXzEYDX3DOnW5m/YH7gIOB3sATQDiUY8Z1pjJkyBA3ffr03Ar+xht+LNwRI3x/wZi0X4RfkeZmv1vggw/8ZdUqP077//7nLzNn+r/L4Id/HzkSvvxlP6ZMyj8iv/0tfP/7viH1nHNa8iVJK+GcP67ukUf8Zdo0f9wF+GPudt/dD3JaX7/1OLxu3aBrV/9PoF27zJe2baFNG79919T46/AS3i8XM5vhnKvYNtxiAv4w4Frn3InB/SsAnHM/TljmsWCZF8ysFlgK9AQuT1w2XC54WMZ1ppJXwDvnB+Pq2jW35cvgxhvhRz/yPRmbm/1l8+bMj2nXzp8udt99fZgPHepHJchp49640b/uuroSlF4ks6Ym31T44ov+cIyFC7deNmwo3/Nm+gEw8z8s+TYjVXrA1xbx2D5A4qmDFgOHpFvGObfJzNYAPYLp/016bDgiSrZ1AmBmZwNnB3fXmtm8hNk7AityfiXxkLHMjY2+ljR3LvzlLy1Yqswq8X2Gyix3JZYZWrDc4T/gdNaty7m2n1jmXYsuWISKCfhUb1Xy34F0y6Sbnmqnb8q/GM65O4E7UxbMbHql/eqqzC2nEstdiWWGyix3JZY5nWJ60SwGdkm43xdIHoR8yzJBE003YFWGx+ayThERyUExAf8SsKeZ9TOzdsBoYErSMlOAM4LbpwJPBXumpwCjzay9mfUD9gRezHGdIiKSg4KbaII29fOBx4A2wF3OVhJPlQAABDxJREFUuTlmdh2+a9EUYDzwJzNrwNfcRwePnWNmDwBzgU3At51zzQCp1llA8VI23cScytxyKrHclVhmqMxyV2KZUyq4F42IiMRb6z2SVUSkyingRUSqVEUHvJmdZmZzzGyzmQ1JmneFmTWY2TwzOzFh+vBgWoOZXd7ypd5eHMsEYGZ3mdn7ZjY7YdoOZva4mb0ZXH8imG5mdmvwGl41s0ERlXkXM3vazF4Pto2LKqTcdWb2opm9EpT7h8H0fmY2LSj3/UHnA4IOCvcH5Z5mZvVRlDsoSxsze9nMHq6EMpvZQjN7zcxmmdn0YFqst4+CRT1WQjEXYB/g08AzwJCE6f2BV/Bj4fQD5uN32rYJbu8GtAuW6R/xa4hdmRLKdjQwCJidMO0mgjGC8Eck/zS4fTLwD/wxDocC0yIq887AoOB2F/zQF/0roNwGdA5ut8WP3XQo8AAwOph+B/Ct4PZ5wB3B7dHA/RFuJ5cA9wIPB/djXWZgIbBj0rRYbx8Fv9aoC1CiDyw54K8Arki4/xhwWHB5LN1yEZU9dmVKKl99UsDPA3YObu8MzAtu/xY/btB2y0Vc/ofwYxtVTLmBjsBM/FHcK4Da5G0l3KaD27XBchZBWfsCTwLHAQ8HQRj3MqcK+IrZPvK5VHQTTQaphlHok2F6lOJYpkx2cs69BxBch+c6jN3rCJoADsTXhmNf7qCpYxbwPvA4/p/daufcphRl22YYECAcBqSl3Qx8DwhHU+pB/MvsgH+Z2YxgyBOogO2jEMUMVdAizOwJ4JMpZl3lnHso3cNSTMtrKIQWlMuQD5UgVq/DzDoDfwUuds59aOkHIYlNuZ0/FmSgmXUHJuObILdbLLiOvNxm9jngfefcDDMbGk5OsWhsyhw4wjn3rpn1Ah43szcyLBuXMhck9gHvnPtMAQ/LNORB3IZCqLThGZaZ2c7OuffMbGd8bRNi9DrMrC0+3Cc45/4WTI59uUPOudVm9gy+zbe7mdUGNd7EsoXlXmzbDgPSko4ARpjZyUAd0BVfo49zmXHOvRtcv29mk/HDllfM9pGPam2iqaShEOJYpkwSh584A9/GHU4fF/Q6OBRYE/7lbUnmq+rjgdedc79MmBX3cvcMau6YWQfgM8DrwNP4YT5g+3KnGgakxTjnrnDO9XXO1eO326ecc2OJcZnNrJOZdQlvA8OA2cR8+yhY1DsBirkAX8D/wm4ElrHtzsqr8G2Y84CTEqafjO9ZMR/fzBOH1xG7MgXlug94D2gK3uev49tMnwTeDK53CJY14PbgNbxGwk7vFi7zkfi/0K8Cs4LLyRVQ7gOAl4NyzwauCabvhq+cNAB/AdoH0+uC+w3B/N0i3laGsrUXTWzLHJTtleAyJ/y+xX37KPSioQpERKpUtTbRiIi0egp4EZEqpYAXEalSCngRkSqlgBcRqVIKeBGRKqWAFxGpUv8fkewTSfWeL54AAAAASUVORK5CYII=\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": 41,
   "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": 42,
   "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>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>569047</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.166485e-42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.192814e-173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569048</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.249728e-42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.378922e-172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569049</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.249728e-42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.378922e-172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569050</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.166485e-42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.192814e-173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569051</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Stainless Steel 310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>PART-1-2</td>\n",
       "      <td>396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time (s)             Material     Alpha      Part  Node    X      Y  \\\n",
       "569047      10.0  Stainless Steel 310  0.000004  PART-1-2   392  0.0  0.008   \n",
       "569048      10.0  Stainless Steel 310  0.000004  PART-1-2   393  0.0  0.006   \n",
       "569049      10.0  Stainless Steel 310  0.000004  PART-1-2   394  0.0  0.004   \n",
       "569050      10.0  Stainless Steel 310  0.000004  PART-1-2   395  0.0  0.002   \n",
       "569051      10.0  Stainless Steel 310  0.000004  PART-1-2   396  0.0  0.000   \n",
       "\n",
       "          Z  Temperature  T_left  ...  Tb+Tt  (sq_x/L)-x/L  (sq_y/L)-y/L  \\\n",
       "569047  0.0        300.0     600  ...    700           0.0         -0.16   \n",
       "569048  0.0        300.0     600  ...    700           0.0         -0.24   \n",
       "569049  0.0        300.0     600  ...    700           0.0         -0.24   \n",
       "569050  0.0        300.0     600  ...    700           0.0         -0.16   \n",
       "569051  0.0        300.0     600  ...    700           0.0          0.00   \n",
       "\n",
       "        deltaT_x/L  Timespacex1   Timespacey1  Timespacex2    Timespacey2  \\\n",
       "569047         0.0          0.0 -4.166485e-42          0.0 -9.192814e-173   \n",
       "569048         0.0          0.0 -6.249728e-42          0.0 -1.378922e-172   \n",
       "569049         0.0          0.0 -6.249728e-42          0.0 -1.378922e-172   \n",
       "569050         0.0          0.0 -4.166485e-42          0.0 -9.192814e-173   \n",
       "569051         0.0          0.0  0.000000e+00          0.0   0.000000e+00   \n",
       "\n",
       "        Timespacex3  Timespacey3  \n",
       "569047          0.0         -0.0  \n",
       "569048          0.0         -0.0  \n",
       "569049          0.0         -0.0  \n",
       "569050          0.0         -0.0  \n",
       "569051          0.0          0.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "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": 45,
   "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": 46,
   "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()"
   ]
  }
 ],
 "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
}