1D 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\\1D_master_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>X</th>\n",
       "      <th>x/L</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_initial</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time(s)   Alpha    X  x/L  T_left  T_right  T_initial  Temperature\n",
       "0     0.01  0.0001  0.0  0.0     300        0         27        300.0\n",
       "1     0.02  0.0001  0.0  0.0     300        0         27        300.0\n",
       "2     0.03  0.0001  0.0  0.0     300        0         27        300.0\n",
       "3     0.05  0.0001  0.0  0.0     300        0         27        300.0\n",
       "4     0.10  0.0001  0.0  0.0     300        0         27        300.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Alpha</th>\n",
       "      <th>X</th>\n",
       "      <th>x/L</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_initial</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5824.000000</td>\n",
       "      <td>5824.000000</td>\n",
       "      <td>5824.000000</td>\n",
       "      <td>5824.000000</td>\n",
       "      <td>5824.000000</td>\n",
       "      <td>5824.000000</td>\n",
       "      <td>5824.0</td>\n",
       "      <td>5824.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.665962</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>210.387281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35.998539</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.003334</td>\n",
       "      <td>0.333362</td>\n",
       "      <td>111.812999</td>\n",
       "      <td>111.812999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.656319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.796381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>308.496669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time(s)        Alpha            X          x/L       T_left  \\\n",
       "count  5824.000000  5824.000000  5824.000000  5824.000000  5824.000000   \n",
       "mean     16.665962     0.000036     0.005000     0.500000   450.000000   \n",
       "std      35.998539     0.000045     0.003334     0.333362   111.812999   \n",
       "min       0.010000     0.000001     0.000000     0.000000   300.000000   \n",
       "25%       0.100000     0.000001     0.001667     0.166667   375.000000   \n",
       "50%       1.000000     0.000010     0.005000     0.500000   450.000000   \n",
       "75%      12.500000     0.000100     0.008333     0.833333   525.000000   \n",
       "max     200.000000     0.000100     0.010000     1.000000   600.000000   \n",
       "\n",
       "           T_right  T_initial  Temperature  \n",
       "count  5824.000000     5824.0  5824.000000  \n",
       "mean    150.000000       27.0   210.387281  \n",
       "std     111.812999        0.0   166.656319  \n",
       "min       0.000000       27.0     0.000000  \n",
       "25%      75.000000       27.0    44.796381  \n",
       "50%     150.000000       27.0   200.000000  \n",
       "75%     225.000000       27.0   308.496669  \n",
       "max     300.000000       27.0   600.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5824, 5), (5824,))"
      ]
     },
     "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.loc[:, ['T_left', 'T_right','Time(s)', 'x/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": [
      "94.58658229764333\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 9.252151489257812\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 97.0605009878433\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     99.0673845257569\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       0.326475\n",
      "1   T_right       0.346833\n",
      "2   Time(s)       1.092091\n",
      "3       x/L    -258.747095\n",
      "4     Alpha  808075.461766\n"
     ]
    }
   ],
   "source": [
    "#Regression Coefficients of the variables in order \n",
    "Coeff_data = {'Variables': ['T_left', 'T_right', 'Time(s)', 'x/L','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": [
       "47.70031814356914"
      ]
     },
     "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": [
       "48.154463928432875"
      ]
     },
     "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": [
      "[259.87882174 317.14276129 339.49135686 ...  86.92370097 247.01932799\n",
      " 131.84042418]\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>640</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>259.878822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>317.142761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>339.491357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4313</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>226.006483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>60.078125</td>\n",
       "      <td>157.725076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4888</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>86.144995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3955</th>\n",
       "      <td>30.378588</td>\n",
       "      <td>184.950519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>49.905918</td>\n",
       "      <td>86.923701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>28.038563</td>\n",
       "      <td>247.019328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4499</th>\n",
       "      <td>27.114826</td>\n",
       "      <td>131.840424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1456 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Actual   Predicted\n",
       "640   300.000000  259.878822\n",
       "2804  350.000000  317.142761\n",
       "2746  400.000000  339.491357\n",
       "4313  400.000000  226.006483\n",
       "1158   60.078125  157.725076\n",
       "...          ...         ...\n",
       "4888   27.000000   86.144995\n",
       "3955   30.378588  184.950519\n",
       "3914   49.905918   86.923701\n",
       "3487   28.038563  247.019328\n",
       "4499   27.114826  131.840424\n",
       "\n",
       "[1456 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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eeOIJzjrrrK3K3nvvPXr06MGaNVv/57yRI0cyZcqUFrUlhSngRUrUpEmTOPnkk6murm627o4GfCF77bUXQ4YM4b777msoW7NmDU899ZQCvJUo4EVK0Nq1a3n66acZP378NgF/4403cvTRR3Pssccybtw4pk6dSk1NDWPGjKGiooJ169ZRXl7OihXhf2/X1NRw6qmnAvDcc89x4oknMnDgQE488UQWLlzY5HyMHj16q/6nTZvGsGHD2HPPPYtq69prr+Wmm7b8U7GjjjqKxYsXA/Db3/6WwYMHU1FRwcUXX8ymTTv6nzp3P7pMUqQ9XXklzJnTum1WVMAttzRZ5b777mPYsGEcdthhdO/endmzZzNo0CAeeugh7rvvPmbOnMmee+7JqlWr6N69Oz//+c+56aabqKysbLLdI444gieffJKysjIeffRRrrnmGu65555G6w8bNoyvfvWrrFy5kh49elBdXc3Xv/717Wora8GCBUyePJmnn36aTp06cemllzJx4kTOP//8oqZPhQJepARNmjSJK6+8EoCqqiomTZrEoEGDePTRR/nyl7/MnnuG/yHevXv3FrW7Zs0axo4dyyuvvIKZsWFDk/9Rjs6dOzN8+HCmTp3K2WefzZw5cxgyZMh2tZX12GOPMWvWLI4//ngA1q1bx4EHHtiiZUmBAl6kPTVzpL0zrFy5khkzZjB37lzMjE2bNmFm3Hjjjbh7UZcMlpWVsXnzZoCtrh//wQ9+wGmnnca0adNYvHhxw6mbpowePZrrrrsOd2fEiBENvyYtpq3sfGTnxd0ZO3Ys//mf/9ls/ynTOXiREjN16lTOP/98Xn/9dRYvXsySJUvo27cvTz31FEOGDGHChAm8//77AKxatQqAvffem3fffbehjfLycmbNmgWw1WmTNWvW0Lt3byB8MVuM0047jVdeeYXbbruN0aNHt6it8vJyZs+eDcDs2bNZtGgRAKeffjpTp07lrbfealiO11/fre/8u11KMuDnzYO8K7NESsakSZP4whe+sFXZ2Wefze9+9zuGDRvG8OHDqayspKKiouELzAsuuIBLLrmk4UvWH/7wh1xxxRV86lOfomPHjg3tfPe73+Xqq6/mpJNOKvpLzQ4dOnD22WezcuVKTjnllBa1dfbZZ7Nq1SoqKiq4/fbbOeywwwDo378/1113HUOGDOGYY47hjDPOYOnS3e7/a++wJP4na2VlpbfkH36YwcCBEN/4RdrUggULOPLII9t7NiRPoe1iZrPcvelvlndhJXkED/DCC+09ByIiO1fJBryISOoU8CIiiVLAi4gkSgEvIpIoBbyISKIU8CIlqGPHjlRUVDQ8Fi9eTE1NDd/4xjeAcEveZ555pqH+fffdx/z581vcT7du3bYpO/XUU3n44Ye3Krvlllu49NJLW9yWNE23KhApQXvssQdz8m5yVl5e3nAzsSeeeIJu3bpx4oknAiHgzzrrLPr377/DfefuIDl06NCGsurqan7yk5/scNuyNR3Biwiw5R9pLF68mDvuuIObb76ZiooK/vSnPzF9+nS+853vUFFRwauvvsqrr77KsGHDOO644/jUpz7V8E9DFi1axCc/+UmOP/54fvCDHxTsZ9SoUfzhD39g/fr1ACxevJg333yTk08+mbVr13L66aczaNAgjj76aO6///5G5zPn8ssvb7iVwaxZs/j0pz/Ncccdx9ChQxt+vXrrrbfSv39/jjnmGKqqqlpzte3SdAQv0o7a6W7BrFu3joqKCgD69u3LtGnTGsaVl5dzySWX0K1bN7797W8DMHz4cM466yxGjRoFhHu93HHHHfTr14+ZM2dy6aWXMmPGDK644gq+9rWvcf7553PbbbcV7LtHjx4MHjyYP/7xj4wYMYLq6mrOPfdczIyuXbsybdo09tlnH1asWMEJJ5zA8OHDi7oB2oYNG/j617/O/fffT8+ePZk8eTLf+973mDBhAtdffz2LFi2iS5curF69upjVmAQFvEgJKnSKplhr167lmWee4Zxzzmkoyx2NP/300w03HzvvvPO46qqrCraRO02TC/gJEyYA4S6Q11xzDU8++SQdOnTgjTfeYPny5Xz0ox9tdr4WLlzI3LlzOeOMMwDYtGkTvXr1AuCYY45hzJgxjBw5kpEjR27Xcu+OFPAi7agd7ha8wzZv3sx+++3X6BtEMUfbI0eO5Fvf+hazZ89m3bp1DBo0CICJEydSX1/PrFmz6NSpE+Xl5VvdjhiavkXwgAED+Mtf/rJNfw888ABPPvkk06dP58c//jHz5s2jrCz9+NM5eBHZRv7tgbPD++yzD3379uX3v/89EIL1xRdfBOCkk05q+Bd8EydObLT9bt26ceqpp/KVr3xlm1sEH3jggXTq1InHH3+84C1+DznkEObPn8/69etZs2YNjz32GACHH3449fX1DQG/YcMG5s2bx+bNm1myZAmnnXYaN954I6tXr2bt2rU7snp2Gwp4EdnG5z//eaZNm0ZFRQV//vOfqaqq4ic/+QkDBw7k1VdfZeLEiYwfP55jjz2WAQMGNHwZ+rOf/YzbbruN448/njXN3JN79OjRvPjii1t96TlmzBhqamqorKxk4sSJHHHEEdtMd/DBB/PFL36x4bTLwIEDgfDfoaZOncpVV13FscceS0VFBc888wybNm3iS1/6EkcffTQDBw7km9/8Jvvtt18rrq1dV8neLhgggUWX3ZBuF7xr0u2CRURkt6GAFxFJlAJepB2kcGo0JaluDwW8SBvr2rUrK1euTDZUdjfuzsqVK+natWt7z0qrK+pCUDMbBvwM6Aj8t7tfnze+C/A/wHHASuBcd18cx10NXAhsAr7h7g+bWVfgSaBLnIep7v7DWL8vUA10B2YD57n7hzu4nCK7jD59+lBXV0d9fX17z4pEXbt2pU+fPu09G62u2YA3s47AbcAZQB3wvJlNd/fsreUuBN5290PNrAq4ATjXzPoDVcAA4CDgUTM7DFgPfMbd15pZJ+ApM3vI3Z+N097s7tVmdkds+/ZWW2KRdtapUyf69u3b3rMhJaCYUzSDgVp3fy0eSVcDI/LqjADujs+nAqdb+DnbCKDa3de7+yKgFhjsQe6XBp3iw+M0n4ltENssnd8Vi4i0omICvjewJDNcF8sK1nH3jcAaoEdT05pZRzObA7wFPOLuM+M0q2MbjfVFnP4iM6sxsxp91BUR2VYxAV/oxhL53w41VqfRad19k7tXAH2AwWZ2VJF9Eae/090r3b2yZ8+ejc68iEipKibg64CDM8N9gDcbq2NmZcC+wKpipnX31cATwDBgBbBfbKOxvkREpAjFBPzzQD8z62tmnQlfmk7PqzMdGBufjwJmeLgGbDpQZWZd4tUx/YDnzKynme0HYGZ7AJ8FXo7TPB7bILa57R3/RUSkWc1eRePuG83scuBhwmWSE9x9npn9CKhx9+nAeOA3ZlZLOHKvitPOM7MpwHxgI3CZu28ys17A3fEKnQ7AFHf/Q+zyKqDazK4DXohti4hIC+lmYyIijdDNxkREZJekgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEFRXwZjbMzBaaWa2ZjSswvouZTY7jZ5pZeWbc1bF8oZkNjWUHm9njZrbAzOaZ2RWZ+tea2RtmNic+PrfjiykiUnrKmqtgZh2B24AzgDrgeTOb7u7zM9UuBN5290PNrAq4ATjXzPoDVcAA4CDgUTM7DNgI/Iu7zzazvYFZZvZIps2b3f2m1lpIEZFSVMwR/GCg1t1fc/cPgWpgRF6dEcDd8flU4HQzs1he7e7r3X0RUAsMdvel7j4bwN3fBRYAvXd8cUREJKeYgO8NLMkM17FtGDfUcfeNwBqgRzHTxtM5A4GZmeLLzewlM5tgZvsXmikzu8jMasyspr6+vojFEBEpLcUEvBUo8yLrNDmtmXUD7gGudPd3YvHtwMeBCmAp8NNCM+Xud7p7pbtX9uzZs+klEBEpQcUEfB1wcGa4D/BmY3XMrAzYF1jV1LRm1okQ7hPd/d5cBXdf7u6b3H0z8CvCKSIREWmhYgL+eaCfmfU1s86EL02n59WZDoyNz0cBM9zdY3lVvMqmL9APeC6enx8PLHD3/8o2ZGa9MoNfAOa2dKFERKSIq2jcfaOZXQ48DHQEJrj7PDP7EVDj7tMJYf0bM6slHLlXxWnnmdkUYD7hypnL3H2TmZ0MnAf81czmxK6ucfcHgRvNrIJwKmcxcHErLq+ISMmwcKC9e6usrPSampqi61v8ZiCBRReRncjMZrl7ZXvPx/bSL1lFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUUUFvJkNM7OFZlZrZuMKjO9iZpPj+JlmVp4Zd3UsX2hmQ2PZwWb2uJktMLN5ZnZFpn53M3vEzF6Jf/ff8cUUESk9zQa8mXUEbgPOBPoDo82sf161C4G33f1Q4Gbghjhtf6AKGAAMA34R29sI/Iu7HwmcAFyWaXMc8Ji79wMei8Otxr01WxMR2XUVcwQ/GKh199fc/UOgGhiRV2cEcHd8PhU43cwslle7+3p3XwTUAoPdfam7zwZw93eBBUDvAm3dDYzcvkUTESltxQR8b2BJZriOLWG8TR133wisAXoUM208nTMQmBmLPuLuS2NbS4EDC82UmV1kZjVmVlNfX1/EYoiIlJZiAt4KlOWf6GisTpPTmlk34B7gSnd/p4h52dKI+53uXunulT179mzJpCIiJaGYgK8DDs4M9wHebKyOmZUB+wKrmprWzDoRwn2iu9+bqbPczHrFOr2At4pdGBER2aKYgH8e6Gdmfc2sM+FL0+l5daYDY+PzUcAMd/dYXhWvsukL9AOei+fnxwML3P2/mmhrLHB/SxdKRESgrLkK7r7RzC4HHgY6AhPcfZ6Z/QiocffphLD+jZnVEo7cq+K088xsCjCfcOXMZe6+ycxOBs4D/mpmc2JX17j7g8D1wBQzuxD4O3BOay6wiEipME/gusHKykqvqakpqq47dOiw5bmISGPMbJa7V7b3fGwv/ZJVRCRRCngRkUQp4EVEEqWAFxFJlAJeRCRRCngRkUQp4EVEEqWAFxFJlAJeRCRRJRfw+vWqiJSKkgt4EZFSoYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRJVcwOsffohIqSi5gBcRKRUKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUlUyQW8/uGHiJSKkgt4EZFSoYAXEUmUAl5EJFEKeBGRRBUV8GY2zMwWmlmtmY0rML6LmU2O42eaWXlm3NWxfKGZDc2UTzCzt8xsbl5b15rZG2Y2Jz4+t/2LJyJSupoNeDPrCNwGnAn0B0abWf+8ahcCb7v7ocDNwA1x2v5AFTAAGAb8IrYHcFcsK+Rmd6+IjwdbtkgiIgLFHcEPBqXboXwAABEASURBVGrd/TV3/xCoBkbk1RkB3B2fTwVONzOL5dXuvt7dFwG1sT3c/UlgVSssg4iIFFBMwPcGlmSG62JZwTruvhFYA/QoctpCLjezl+JpnP0LVTCzi8ysxsxq6uvri2hSRKS0FBPwVqAs/+dCjdUpZtp8twMfByqApcBPC1Vy9zvdvdLdK3v27NlMkyIipaeYgK8DDs4M9wHebKyOmZUB+xJOvxQz7Vbcfbm7b3L3zcCviKd0RESkZYoJ+OeBfmbW18w6E740nZ5XZzowNj4fBcxwd4/lVfEqm75AP+C5pjozs16ZwS8AcxurKyIijStrroK7bzSzy4GHgY7ABHefZ2Y/AmrcfTowHviNmdUSjtyr4rTzzGwKMB/YCFzm7psAzGwScCpwgJnVAT909/HAjWZWQTiVsxi4uDUXWESkVJgncPetyspKr6mpKaruxo3QqVN4nsCii8hOZGaz3L2yvedje+mXrCIiiVLAi4gkSgEvIpKokgt4nXcXkVJRcgEvIlIqFPAiIolSwIuIJEoBLyKSKAW8iEiiFPAiIolSwIuIJEoBLyKSKAW8iEiiFPAiIolSwIuIJEoBLyKSKAW8iEiiFPAiIolSwIuIJEoBLyKSqJILeP3DDxEpFSUX8CIipUIBLyKSKAW8iEiiFPAiIolSwIuIJEoBLyKSKAW8iEiiFPAiIolSwIuIJEoBLyKSKAW8iEiiFPAiIolSwIuIJEoBLyKSKAW8iEiiSi7gdT94ESkVJRfwIiKlQgEvIpIoBbyISKIU8CIiiVLAi4gkSgEvIpIoBbyISKIU8CIiiVLAi4gkSgEvIpIoBbyISKKKCngzG2ZmC82s1szGFRjfxcwmx/Ezzaw8M+7qWL7QzIZmyieY2VtmNjevre5m9oiZvRL/7r/9iyciUrqaDXgz6wjcBpwJ9AdGm1n/vGoXAm+7+6HAzcANcdr+QBUwABgG/CK2B3BXLMs3DnjM3fsBj8VhERFpoWKO4AcDte7+mrt/CFQDI/LqjADujs+nAqebmcXyandf7+6LgNrYHu7+JLCqQH/Ztu4GRrZgeUREJCom4HsDSzLDdbGsYB133wisAXoUOW2+j7j70tjWUuDAQpXM7CIzqzGzmvr6+iIWQ0SktBQT8FagLP+u6o3VKWba7eLud7p7pbtX9uzZszWaFBFJSjEBXwccnBnuA7zZWB0zKwP2JZx+KWbafMvNrFdsqxfwVhHzWDT9ww8RKRXFBPzzQD8z62tmnQlfmk7PqzMdGBufjwJmuLvH8qp4lU1foB/wXDP9ZdsaC9xfxDyKiEieZgM+nlO/HHgYWABMcfd5ZvYjMxseq40HephZLfAt4pUv7j4PmALMB/4IXObumwDMbBLwF+BwM6szswtjW9cDZ5jZK8AZcVhERFrIPIFzFpWVlV5TU1NU3Q8+gD32CM8TWHQR2YnMbJa7V7b3fGwv/ZJVRCRRCngRkUQp4EVEEqWAFxFJlAJeRCRRCngRkUQp4EVEEqWAFxFJlAJeRCRRCngRkUQp4EVEEqWAFxFJlAJeRCRRJRfwuoOkiJSKkgt4EZFSoYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRJVcwOsffohIqSi5gBcRKRUKeBGRRCngRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSVXIBr+vgRaRUlFzAi4iUCgW8iEiiFPAiIolSwIuIJEoBLyKSKAW8iEiiFPAiIokquYDXdfAiUiqKCngzG2ZmC82s1szGFRjfxcwmx/Ezzaw8M+7qWL7QzIY216aZ3WVmi8xsTnxU7NgiioiUprLmKphZR+A24AygDnjezKa7+/xMtQuBt939UDOrAm4AzjWz/kAVMAA4CHjUzA6L0zTV5nfcfWorLJ+ISMkq5gh+MFDr7q+5+4dANTAir84I4O74fCpwuplZLK929/Xuvgioje0V06aIiOyAYgK+N7AkM1wXywrWcfeNwBqgRxPTNtfmv5vZS2Z2s5l1KTRTZnaRmdWYWU19fX0RiyEiUlqKCXgrUJb/VWVjdVpaDnA1cARwPNAduKrQTLn7ne5e6e6VPXv2LFRFRKSkFRPwdcDBmeE+wJuN1TGzMmBfYFUT0zbaprsv9WA98GvC6RwREWmhYgL+eaCfmfU1s86EL02n59WZDoyNz0cBM9zdY3lVvMqmL9APeK6pNs2sV/xrwEhg7o4soIhIqWr2Khp332hmlwMPAx2BCe4+z8x+BNS4+3RgPPAbM6slHLlXxWnnmdkUYD6wEbjM3TcBFGozdjnRzHoSTuPMAS5pvcXVdfAiUjrME0i8yspKr6mpKaruu+/CPvuE5wksuojsRGY2y90r23s+tlfJ/ZJVRKRUKOBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBKlgBcRSVTJBbyufReRUlFyAS8iUioU8CIiiVLAi4gkSgEvIpIoBbyISKIU8CIiiVLAi4gkquQCXtfBi0ipKLmAFxEpFQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBJVcgGv6+BFpFSUXMCLiJQKBbyISKIU8CIiiVLAi4gkSgEvIpIoBbyISKIU8CIiiSq5gNd18CJSKkou4EVESoUCXkQkUQp4EZFEKeBFRBKlgBcRSZQCXkQkUQp4EZFEKeBFRBJVcgGvHzqJSKkouYAXESkVCngRkUQp4EUkfZs2leT5WQW8iKRv/Hjo3x/q69t7TtqUAl5E0jd5MmzeDAcc0N5z0qYU8CKStuXL4Ykn4ItfBLP2nps2pYAXkbTde284ej/33PaekzZXVMCb2TAzW2hmtWY2rsD4LmY2OY6faWblmXFXx/KFZja0uTbNrG9s45XYZucdW8StleD3LCKlbcoUOPJIGDCgveekzTUb8GbWEbgNOBPoD4w2s/551S4E3nb3Q4GbgRvitP2BKmAAMAz4hZl1bKbNG4Cb3b0f8HZsu9VsFfB9+sB558Err8Ds2fDLX8KMGfD++7BsGTz7LCxaFL6BX7s2PH/77dDIhg2wYgV88EFoa/PmUGfjxi0drV8fynM2b9Y7jJQm921fC+vXb3k9bNgA77235TXy/vuwcmV4PbmH19rixaF8w4bwmp05M5S/+y788Y/wq1/B3Lnw0kswciSUl8O//Rv86U8leXoGoKyIOoOBWnd/DcDMqoERwPxMnRHAtfH5VODnZmaxvNrd1wOLzKw2tkehNs1sAfAZ4J9inbtju7dv19IVsHnBQuDwMHDYYXDPPfDb3zY9UYcOW++cnTqFnSync2f48MMtw127huHcNHvsEepv3Bja6to1/M1x3/ICyO3gub9moW7ub+55bpps3ZzcjmzW+PNcv039zZd9geS3lf+8kMba3Z43vcb6asmLeFeYn/y+mhpubr6a2ybZ57l9J/s8vyw3Te6R2/fMtuyr2Yf7ln00+3APB0LuUFYWHrkDow4dwutp/fot/ZWVbf36KivbcuCUmyb7eixk333h6KPh2mvD8DnnNF0/UcUEfG9gSWa4DvhEY3XcfaOZrQF6xPJn86btHZ8XarMHsNrdNxaovxUzuwi4KA6uNbOFRSxL1gH2OCuKqpm/M2V3Ptg63GHLzpuzbt3Wbb3//gFQZN8QPkG0npb13fras38t+86WC/vG+t64ceuwzh3J5+Q+HWdl6+emac6aNfDUU1v6P+qo7V32Q7Zzul1CMQFf6BAk/1CisTqNlRc6NdRU/W0L3e8E7iw0rhhmVuPulds7/Y4o1b7bu38te+n1vSv0356K+ZK1Djg4M9wHeLOxOmZWBuwLrGpi2sbKVwD7xTYa60tERIpQTMA/D/SLV7d0JnxpOj2vznRgbHw+Cpjh7h7Lq+JVNn2BfsBzjbUZp3k8tkFs8/7tXzwRkdLV7CmaeE79cuBhoCMwwd3nmdmPgBp3nw6MB34Tv0RdRQhsYr0phC9kNwKXufsmgEJtxi6vAqrN7Drghdj2zrDdp3fU927bv5a99PreFfpvN+a6bE9EJEn6JauISKIU8CIiqXL3knoQflG7EKgFxrVw2gnAW8DcTFl34BHglfh3/1huwK2xn5eAQZlpxsb6rwBjM+XHAX+N09zKllNo3YEngfeBtcAC4Io27v8x4APg3dj/v8VxfYGZsa3JQOdY3iUO18bx5Zl+ro7lC4GhzW2bvD7eBh5oh743AKuBFwnfPbXlun88rvf34jx+so36Hhz7XR//vgNc2YbL/QhQT9jn5wOTgK5tvN236mN3e7T7DLTpwoYvdF8FPgZ0ji/W/i2Y/hRgEFsH/I25HQMYB9wQn38OeCju9CcAMzM77mvx7/7xee4F8lx88Vqc9sxMH/8e+x5HuB3E3wi3eWir/scB3eLfn8Sd/wRgClAV690BfC0+vxS4Iz6vAibH5/3jeu8SX0Svxu3S6LbJ9QF8K77g/potb6O+FwN35fpo420/C/hqZt3v15bbPT6/mhC0h7Rh3/8BLAK+T7iFyRTggrbc7vl97G6Pdp+BNl3YsCM9nBm+Gri6hW2Us3XALwR6xee9gIXx+S+B0fn1gNHALzPlv4xlvYCXM+UN9Qr1Qbh89Ix26v9vwGzCr49XAGX565dwhdQn4/OyWM/y13muXmPbJk6zghAsjwGXEz5FWRv2XUYI+GF59dpi3b8C/D3OS7aPtt7uVcC6Nu57IOEX70fG4T8AQ9tyuxfKjd3pUWrn4AvddqHgrRBa4CPuvhQg/j2wmb6aKq9rZN7y+/goYeef2Zb9x5vEPUT4PcMjhKOfxm4tsdXtK4Ds7StaMl89CKdGfgp8F1hO+Jje1G0tWrXv2IYTjp5PibfJaFgvsZ+dte4/CiwDfg08CJSb2V5t1HdDH8AQtvyqvK36fgG4ifi7GcJ2nEXbbvf8PnYrpRbwRd8KYSf21dLyrRs16wbsBVzp7u+0Zf/uvsndKwgvnMGEI6vGpmmt/g3YE3jL3WdlxjU1v63Zd85JhFMQi4DLzOyUAtM0N2/bu+4HAbe7+8BYvs0tu3di38QfIw4HPixQd6f1bWb7E25Y2Jewz+1FuANtY9PsjO2+zXztTkot4Iu57UJLLTezXgDx71vN9NVUeZ9G5m25mfUys07A/wNWufu9bd1/po/lwBOE86yN3VqiVW9fAQw3s8WE86F7Abe0Vd9mVubub8byJcA0whtcm6x7YKm7z8z0Mait+o5tnwnMjfOSLd/Zyz2K8IZaFvu4FziRNtzuBfrYrZRawBdz24WWyt6mYSxbbq0wHTjfghOANfHj7MPAEDPbPx6hDCGc31sKvGtmJ8RbLZ+f19ZYwq96OxG+7Gvr/i81s/1iHw8AnyVcTfM4hW8t0Zq3r3gA+La7lwNPA/PdfUwb9f04MMbM9o5tPhTX2dw2WvfTgI1mdnjsYwnhipK23O9GE65mKbR+d2bfFYSDiK/G8tPjsrfVdt/9b5nS3l8CtPWD8DH7b4Tzx99r4bSTgKWES+bqCP+MpAfhy79X4t/usa4R/qnJq4TLwCoz7XyFcFlWLfDlTHklITheBX7OlkvGegA1hI+J78b25sRlaav+Z7L1ZZL/Gsd9jPBiqQV+D3SJ5V3jcG0c/7FMP9+LfSwkXjXR1LbJ6+MJ4ME27vtFwqWCqwkB873Memnrdf8A4UqUtur7cWBTXO/d23i5HwNWEi4PXQD8hnAlTFvvcw197G4P3apARCRRpXaKRkSkZCjgRUQSpYAXEUmUAl5EJFEKeBGRRCngRUQSpYAXEUnU/wcpOW1kOextZwAAAABJRU5ErkJggg==\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\n",
    "scaler = StandardScaler()\n",
    "scaled = scaler.fit_transform(data)"
   ]
  },
  {
   "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>Alpha</th>\n",
       "      <th>X</th>\n",
       "      <th>x/L</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_initial</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>scaled_T_left</th>\n",
       "      <th>scaled_T_right</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>scaled_location</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>0.000100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-0.462724</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.429927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-0.462446</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.429927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-0.462168</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.429927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-0.461613</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.429927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-1.341641</td>\n",
       "      <td>-0.460224</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.429927</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819</th>\n",
       "      <td>20.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>0.092624</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.792677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5820</th>\n",
       "      <td>30.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>0.370437</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.792677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5821</th>\n",
       "      <td>50.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>0.926063</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.792677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5822</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>2.315127</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.792677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5823</th>\n",
       "      <td>200.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>5.093256</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.792677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5824 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time(s)     Alpha     X  x/L  T_left  T_right  T_initial  Temperature  \\\n",
       "0        0.01  0.000100  0.00  0.0     300        0         27        300.0   \n",
       "1        0.02  0.000100  0.00  0.0     300        0         27        300.0   \n",
       "2        0.03  0.000100  0.00  0.0     300        0         27        300.0   \n",
       "3        0.05  0.000100  0.00  0.0     300        0         27        300.0   \n",
       "4        0.10  0.000100  0.00  0.0     300        0         27        300.0   \n",
       "...       ...       ...   ...  ...     ...      ...        ...          ...   \n",
       "5819    20.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5820    30.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5821    50.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5822   100.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5823   200.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "\n",
       "      scaled_T_left  scaled_T_right  scaled_time  scaled_location  \\\n",
       "0         -1.341641       -1.341641    -0.462724             -1.5   \n",
       "1         -1.341641       -1.341641    -0.462446             -1.5   \n",
       "2         -1.341641       -1.341641    -0.462168             -1.5   \n",
       "3         -1.341641       -1.341641    -0.461613             -1.5   \n",
       "4         -1.341641       -1.341641    -0.460224             -1.5   \n",
       "...             ...             ...          ...              ...   \n",
       "5819       1.341641        1.341641     0.092624              1.5   \n",
       "5820       1.341641        1.341641     0.370437              1.5   \n",
       "5821       1.341641        1.341641     0.926063              1.5   \n",
       "5822       1.341641        1.341641     2.315127              1.5   \n",
       "5823       1.341641        1.341641     5.093256              1.5   \n",
       "\n",
       "      scaled_alpha  \n",
       "0         1.429927  \n",
       "1         1.429927  \n",
       "2         1.429927  \n",
       "3         1.429927  \n",
       "4         1.429927  \n",
       "...            ...  \n",
       "5819     -0.792677  \n",
       "5820     -0.792677  \n",
       "5821     -0.792677  \n",
       "5822     -0.792677  \n",
       "5823     -0.792677  \n",
       "\n",
       "[5824 rows x 13 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_time']     = StandardScaler().fit_transform(data[['Time(s)']])\n",
    "data['scaled_location'] = StandardScaler().fit_transform(data[['x/L']])\n",
    "data['scaled_alpha']    = StandardScaler().fit_transform(data[['Alpha']])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5824, 5), (5824,))"
      ]
     },
     "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.loc[:, ['scaled_T_left', 'scaled_T_right','scaled_time', 'scaled_location', '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: 97.769\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.loc[:, ['scaled_T_left', 'scaled_T_right','scaled_time', 'scaled_location', '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": [
      "211.69180085572307\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 2.438900947570801\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 97.06050098784324\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     99.06738452575686\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([ 36.50101006,  38.77709098,  39.31031753, -86.24903159,\n",
       "        35.99357793])"
      ]
     },
     "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    36.501010\n",
      "1   scaled_T_right    38.777091\n",
      "2      scaled_time    39.310318\n",
      "3  scaled_location   -86.249032\n",
      "4     scaled_Alpha    35.993578\n"
     ]
    }
   ],
   "source": [
    "#Regression Coefficients of the variables in order \n",
    "Coeff_data = {'Variables': ['scaled_T_left', 'scaled_T_right', 'scaled_time', 'scaled_location','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": [
       "47.70031814356914"
      ]
     },
     "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": [
       "48.154463928432854"
      ]
     },
     "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": [
      "[259.87882174 317.14276129 339.49135686 ...  86.92370097 247.01932799\n",
      " 131.84042418]\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>640</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>259.878822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>317.142761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>339.491357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4313</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>226.006483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>60.078125</td>\n",
       "      <td>157.725076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4888</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>86.144995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3955</th>\n",
       "      <td>30.378588</td>\n",
       "      <td>184.950519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>49.905918</td>\n",
       "      <td>86.923701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>28.038563</td>\n",
       "      <td>247.019328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4499</th>\n",
       "      <td>27.114826</td>\n",
       "      <td>131.840424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1456 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Actual   Predicted\n",
       "640   300.000000  259.878822\n",
       "2804  350.000000  317.142761\n",
       "2746  400.000000  339.491357\n",
       "4313  400.000000  226.006483\n",
       "1158   60.078125  157.725076\n",
       "...          ...         ...\n",
       "4888   27.000000   86.144995\n",
       "3955   30.378588  184.950519\n",
       "3914   49.905918   86.923701\n",
       "3487   28.038563  247.019328\n",
       "4499   27.114826  131.840424\n",
       "\n",
       "[1456 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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\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['Tl-Tr'] = data['T_left'] - data['T_right']\n",
    "\n",
    "data['Tl+Tr'] = data['T_left'] + data['T_right']\n",
    "\n",
    "data['(sq_x/L)-x/L'] = data['sq_x/L'] - data['x/L']\n",
    "\n",
    "data['deltaT_x/L'] = data['Tl-Tr'] * data['x/L']\n",
    "\n",
    "data['Timespace1'] = data['(sq_x/L)-x/L'] * data['Tl+Tr'] * data['exp t1'] \n",
    "\n",
    "data['Timespace2'] = data['(sq_x/L)-x/L'] * data['Tl-Tr'] * data['exp t2']\n",
    "\n",
    "data['Timespace3'] = data['(sq_x/L)-x/L'] * data['Tl+Tr'] * 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>Alpha</th>\n",
       "      <th>X</th>\n",
       "      <th>x/L</th>\n",
       "      <th>T_left</th>\n",
       "      <th>T_right</th>\n",
       "      <th>T_initial</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>scaled_T_left</th>\n",
       "      <th>scaled_T_right</th>\n",
       "      <th>...</th>\n",
       "      <th>exp t2</th>\n",
       "      <th>exp t3</th>\n",
       "      <th>sq_x/L</th>\n",
       "      <th>Tl-Tr</th>\n",
       "      <th>Tl+Tr</th>\n",
       "      <th>(sq_x/L)-x/L</th>\n",
       "      <th>deltaT_x/L</th>\n",
       "      <th>Timespace1</th>\n",
       "      <th>Timespace2</th>\n",
       "      <th>Timespace3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5807</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>4.493290e-01</td>\n",
       "      <td>1.652989e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5808</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>3.011942e-01</td>\n",
       "      <td>6.720551e-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5809</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>1.353353e-01</td>\n",
       "      <td>1.110900e-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5810</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>1.831564e-02</td>\n",
       "      <td>1.234098e-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5811</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>3.354626e-04</td>\n",
       "      <td>1.522998e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5812</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>6.144212e-06</td>\n",
       "      <td>1.879529e-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5813</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>2.061154e-09</td>\n",
       "      <td>2.862519e-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5814</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>4.248354e-18</td>\n",
       "      <td>8.194013e-40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5815</th>\n",
       "      <td>2.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>1.804851e-35</td>\n",
       "      <td>6.714184e-79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5816</th>\n",
       "      <td>3.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>7.667648e-53</td>\n",
       "      <td>5.501611e-118</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5817</th>\n",
       "      <td>5.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>1.383897e-87</td>\n",
       "      <td>3.693883e-196</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5818</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>1.915170e-174</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819</th>\n",
       "      <td>20.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5820</th>\n",
       "      <td>30.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5821</th>\n",
       "      <td>50.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5822</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5823</th>\n",
       "      <td>200.00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600</td>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300</td>\n",
       "      <td>900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time(s)     Alpha     X  x/L  T_left  T_right  T_initial  Temperature  \\\n",
       "5807     0.02  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5808     0.03  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5809     0.05  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5810     0.10  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5811     0.20  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5812     0.30  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5813     0.50  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5814     1.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5815     2.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5816     3.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5817     5.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5818    10.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5819    20.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5820    30.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5821    50.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5822   100.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "5823   200.00  0.000001  0.01  1.0     600      300         27        300.0   \n",
       "\n",
       "      scaled_T_left  scaled_T_right  ...         exp t2         exp t3  \\\n",
       "5807       1.341641        1.341641  ...   4.493290e-01   1.652989e-01   \n",
       "5808       1.341641        1.341641  ...   3.011942e-01   6.720551e-02   \n",
       "5809       1.341641        1.341641  ...   1.353353e-01   1.110900e-02   \n",
       "5810       1.341641        1.341641  ...   1.831564e-02   1.234098e-04   \n",
       "5811       1.341641        1.341641  ...   3.354626e-04   1.522998e-08   \n",
       "5812       1.341641        1.341641  ...   6.144212e-06   1.879529e-12   \n",
       "5813       1.341641        1.341641  ...   2.061154e-09   2.862519e-20   \n",
       "5814       1.341641        1.341641  ...   4.248354e-18   8.194013e-40   \n",
       "5815       1.341641        1.341641  ...   1.804851e-35   6.714184e-79   \n",
       "5816       1.341641        1.341641  ...   7.667648e-53  5.501611e-118   \n",
       "5817       1.341641        1.341641  ...   1.383897e-87  3.693883e-196   \n",
       "5818       1.341641        1.341641  ...  1.915170e-174   0.000000e+00   \n",
       "5819       1.341641        1.341641  ...   0.000000e+00   0.000000e+00   \n",
       "5820       1.341641        1.341641  ...   0.000000e+00   0.000000e+00   \n",
       "5821       1.341641        1.341641  ...   0.000000e+00   0.000000e+00   \n",
       "5822       1.341641        1.341641  ...   0.000000e+00   0.000000e+00   \n",
       "5823       1.341641        1.341641  ...   0.000000e+00   0.000000e+00   \n",
       "\n",
       "      sq_x/L  Tl-Tr  Tl+Tr  (sq_x/L)-x/L  deltaT_x/L  Timespace1  Timespace2  \\\n",
       "5807     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5808     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5809     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5810     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5811     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5812     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5813     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5814     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5815     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5816     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5817     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5818     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5819     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5820     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5821     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5822     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "5823     1.0    300    900           0.0       300.0         0.0         0.0   \n",
       "\n",
       "      Timespace3  \n",
       "5807         0.0  \n",
       "5808         0.0  \n",
       "5809         0.0  \n",
       "5810         0.0  \n",
       "5811         0.0  \n",
       "5812         0.0  \n",
       "5813         0.0  \n",
       "5814         0.0  \n",
       "5815         0.0  \n",
       "5816         0.0  \n",
       "5817         0.0  \n",
       "5818         0.0  \n",
       "5819         0.0  \n",
       "5820         0.0  \n",
       "5821         0.0  \n",
       "5822         0.0  \n",
       "5823         0.0  \n",
       "\n",
       "[17 rows x 24 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(17)"
   ]
  },
  {
   "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', fontsize=14)\n",
    "plt.xlabel('Location', 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['Timespace1'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Timespace Parameter 1', fontsize=14)\n",
    "plt.xlabel('Timespace1', 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['Timespace2'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Timespace Parameter 2', fontsize=14)\n",
    "plt.xlabel('Timespace2', 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['Timespace3'], data['Temperature'], color='green')\n",
    "plt.title('Temperature Vs Timespace Parameter 3', fontsize=14)\n",
    "plt.xlabel('Timespace3', fontsize=14)\n",
    "plt.ylabel('Temperature', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5824, 6), (5824,))"
      ]
     },
     "execution_count": 47,
     "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', 'Alpha', 'deltaT_x/L','Timespace1', 'Timespace2', 'Timespace3']]\n",
    "y = data['Temperature']\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-17.399506662003574\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": 51,
   "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": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error 61.34834008687789\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": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Absolute Error     60.31800585234076\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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.28598593e-01,  7.34208998e+05, -8.65068332e-01,  2.92989266e+00,\n",
       "       -9.45872208e-01, -1.57132942e+00])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Variables    Coefficient\n",
      "0      T_left       0.828599\n",
      "1  deltaT_x/L  734208.998203\n",
      "2       Alpha      -0.865068\n",
      "3  Timespace1       2.929893\n",
      "4  Timespace2      -0.945872\n",
      "5  Timespace3      -1.571329\n"
     ]
    }
   ],
   "source": [
    "#Regression Coefficients of the variables in order \n",
    "Coeff_data = {'Variables': ['T_left', 'deltaT_x/L', 'Alpha', 'Timespace1', 'Timespace2','Timespace3'], 'Coefficient': lr.coef_}\n",
    "df = pd.DataFrame(Coeff_data, columns = ['Variables', 'Coefficient'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficient plot')"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.63110580282039"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_x, train_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.38855940797147"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_x,test_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-17.399506692414803\n"
     ]
    }
   ],
   "source": [
    "# Creating instance of Linear Regresssion\n",
    "lr_2 = LR(normalize = True)\n",
    "\n",
    "# Fitting the model\n",
    "lr_2.fit(train_x, train_y)\n",
    "print (lr_2.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error 61.34834008580323\n"
     ]
    }
   ],
   "source": [
    "# Predicting over the Train Set and calculating error\n",
    "train_predict = lr_2.predict(train_x)\n",
    "k = mae(train_predict, train_y)\n",
    "print('Training Mean Absolute Error', k )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Absolute Error     60.318005851604326\n"
     ]
    }
   ],
   "source": [
    "# Predicting over the Test Set and calculating error\n",
    "test_predict = lr_2.predict(test_x)\n",
    "k = mae(test_predict, test_y)\n",
    "print('Test Mean Absolute Error    ', k )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Coefficients')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4JQ8PD9u4JJWXl9da16JFCzVv3txWe/LkSVmt1jp7Xu515Z911R46dMhu/nvvvfe6PeuSnp6uxMTEa9YAAAAAuL00qhB28eJFVVZWatGiRUpISJAkhYeHq3nz5po1a5YKCgpstSaTqdYeVx+vq+6nqnWk59ViY2Nr3Mq4f/9+jR079prnAQAAAGi8GtXtiJ6enpKk4cOH2x2//KKLXbt22Wpq20U6ceKE3Q6Vp6dnrXVnzpzR+fPnbbVt2rSRyWSqs6f04+5VQ8x/dc+6eHt7q2fPnnY/Xbt2veY5AAAAABq3RhXCansmS5Lt9fR33XWXAgICJEm7d++uUbd7927buCQFBgaqrKxMpaWlNeok2WpdXV3VtWvXOnu6urrabisMDAysdf7q6mrt2bOnxvz/+c9/bC/1qGt+AAAAAHeORhXCxo0bJ0navHmz3fGcnBxJ0sMPPywfHx+FhIQoMzNTFy5csNXs2LFDe/futftW15gxY2QymbR69Wq7fhkZGXJ1dVVERITtWGRkpD744AN9/fXXtmM//PCD1q9fr1/+8pe2l2v07dtXHTp0UEZGhl3PdevW6fTp03bzR0ZG6vTp0/rHP/5hV7t69Wp17NhRffv2veFrAwAAAKBpaFTPhA0bNkyjR4/Wiy++qIsXL+rhhx/WJ598osTERI0aNUqPPPKIJGnJkiUaOnSooqOjFRsbK4vFooSEBAUEBNi9Or5nz56aNm2aFi1aJCcnJwUHB2vLli1asWKFFi9ebHc74OzZs/Xmm29q5MiRevHFF2U2m5WSkqLKykq98MILtjonJye98sormjRpkp566ik9+uij+vLLLzV37lwNHTrULtiNGDFCQ4cO1YwZM/T999+ra9euWrNmjXJzc5WZmVnvb4QBAAAAuP01qhAmSWvXrlViYqJWrFihxMREdezYUc8884wWLVpkqwkPD1dOTo4WLlyo0aNHy83NTaNGjdLSpUtlNpvt+qWnp8vHx0dpaWkqLS2Vr6+vUlNTNXPmTLu6tm3b6sMPP9Ts2bP1+OOPq7q6WqGhodq2bZv8/f3taidOnCgnJyelpKQoIyNDHh4emjx5spKSkmr896xfv17z58/XwoULdeLECfn7+2vNmjWaMGFCA141AAAAALcLk/XyA1e4LdzMF7mBG+WbsOlWL+GOcyhl5K1eAgAAcMDN/H7eqJ4JAwAAAICmjhAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGalQhbNu2bTKZTLX+7Nixw642Pz9foaGhcnNzk5eXl6ZMmSKLxVKjZ1VVlRITE+Xr6yuz2Sx/f3+lpaXVOv/BgwcVFRWl1q1by93dXUOHDtWuXbtqrX377bfVu3dvubi4qGPHjpo1a5ZOnz5do+706dOaNWuWOnbsKBcXF/Xu3Vtvv/32TVwdAAAAAE2B861eQG2Sk5M1cOBAu2MBAQG2vxcWFmrEiBEaOXKksrOzZbFYNG/ePA0ePFiffPKJzGazrTY2NlZvvvmmXnrpJQUHBysvL0/x8fH64Ycf9Nxzz9nqysrKFBYWpjZt2mjVqlVycXHRyy+/rPDwcBUVFalHjx622r///e+aOHGipk+frj/+8Y/at2+f5s2bp3//+9/asmWL3bqjoqJUVFSklJQUde/eXW+99ZYeffRRXbx4Ub/5zW8a+tIBAAAAaOQaZQjr1q2bHn744TrH58yZo+7du2vdunVydr70n+Dn56f+/ftr1apVmjFjhiSppKREK1euVFJSkubMmSNJCg8PV3l5uRYvXqynn35aHh4ekqSlS5eqrKxM27dvV+fOnSVJjzzyiLp06aKFCxdq7dq1kqQLFy5ozpw5GjZsmP72t79JkgYOHKiWLVvqscce0+bNmzVixAhJUk5Ojt5//31b8Lpc+9VXX2nOnDn69a9/LScnp4a+fAAAAAAasUZ1O+KNOHLkiIqKijRp0iRbAJOkfv36qXv37srKyrId27Bhg6xWq2JiYux6xMTEqKKiQrm5ubZjWVlZGjRokC2ASVKrVq0UFRWl9957T9XV1ZKkHTt26Ntvv63RMzo6Wu7u7nbzZ2Vlyd3dXdHR0TXmP3r0qHbu3OnAlQAAAABwO2qUISwuLk7Ozs5q1aqVhg8frn/961+2seLiYklSUFBQjfOCgoJs45dr27Ztq/bt29eou7JXRUWFDhw4UGfPiooKHTx48JrzN2vWTP7+/jXmv+++++zCYm3z18VisaikpMTuZ//+/dc8BwAAAEDj1qhuR/zZz36m+Ph4hYeHy9PTU/v379fSpUsVHh6uTZs2afjw4SovL5ck222EV/Lw8LCNS1J5eXmtdS1atFDz5s1ttSdPnpTVaq2z5+VeV/5ZV+2hQ4fs5r/33nuv27Mu6enpSkxMvGYNAAAAgNtLowphDzzwgB544AHbv8PCwhQZGanAwEDNnTtXw4cPt42ZTKZae1x9vK66n6rWkZ5Xi42NrXEr4/79+zV27NhrngcAAACg8WpUIaw2rVu31qhRo/TXv/5VFRUV8vT0lFT7LtKJEyfsdqg8PT312Wef1ag7c+aMzp8/b6tt06aNTCZTnT2lH3evrpy/Xbt2153/RnrWxdvbW97e3tesAQAAAHB7aZTPhF3NarVKurRzdPlV9bt3765Rt3v3brtX2QcGBqqsrEylpaU16qQfX3vv6uqqrl271tnT1dXVdlthYGBgrfNXV1drz549Neb/z3/+Y3upR13zAwAAALhzNPoQdvLkSW3cuNH2YWQfHx+FhIQoMzNTFy5csNXt2LFDe/fuVVRUlO3YmDFjZDKZtHr1arueGRkZcnV1VUREhO1YZGSkPvjgA3399de2Yz/88IPWr1+vX/7yl7aXa/Tt21cdOnRQRkaGXc9169bp9OnTdvNHRkbq9OnT+sc//mFXu3r1anXs2FF9+/a9+QsDAAAA4LbUqG5H/M1vfqNOnTrpoYcekpeXl7788kv94Q9/0LFjx+xCz5IlSzR06FBFR0crNjZWFotFCQkJCggIsHt1fM+ePTVt2jQtWrRITk5OCg4O1pYtW7RixQotXrzY7nbA2bNn680339TIkSP14osvymw2KyUlRZWVlXrhhRdsdU5OTnrllVc0adIkPfXUU3r00Uf15Zdfau7cuRo6dKhdsBsxYoSGDh2qGTNm6Pvvv1fXrl21Zs0a5ebmKjMzk2+EAQAAAHegRhXCgoKCtHbtWv31r3/V6dOn5eHhoUceeURvvvmmgoODbXXh4eHKycnRwoULNXr0aLm5uWnUqFFaunSpzGazXc/09HT5+PgoLS1NpaWl8vX1VWpqqmbOnGlX17ZtW3344YeaPXu2Hn/8cVVXVys0NFTbtm2Tv7+/Xe3EiRPl5OSklJQUZWRkyMPDQ5MnT1ZSUlKN/6b169dr/vz5WrhwoU6cOCF/f3+tWbNGEyZMaMArBwAAAOB2YbJefuAKt4WSkhIFBASouLhYPXv2vNXLQRPjm7DpVi/hjnMoZeStXgIAAHDAzfx+3uifCQMAAACApoQQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgQhgAAAAAGIgQBgAAAAAGIoQBAAAAgIEIYQAAAABgIEIYAAAAABiIEAYAAAAABiKEAQAAAICBCGEAAAAAYCBCGAAAAAAYiBAGAAAAAAYihAEAAACAgQhhAAAAAGAgh0LY0aNHtXfvXtu/L1y4oFdeeUUTJkzQqlWrHF4cAAAAADQ1zo6c/NRTT6lTp05avny5JOmll17Siy++qNatW+vdd99V8+bNNXHixAZZKAAAAAA0BQ7thO3atUsDBw60/ftvf/ubnnnmGZ04cUJPPvmkLZwBAAAAAC5xKISVl5erffv2kqT//Oc/+vbbbzVlyhRJ0rhx4+xuVQQAAAAAOBjCfvazn8lisUiS/vnPf8rDw0OBgYGSJJPJpPPnzzu+QgAAAABoQhx6JiwkJERLlixRs2bNlJqaqmHDhtnGDh48qI4dOzq8QAAAAABoShzaCXvxxRd18OBBjRkzRseOHdP8+fNtYxs2bFBISIjDCwQAAACApsShnbAHHnhAX331lfbs2aOuXbuqVatWtrHY2Fh169bN4QUCAAAAQFPi0E7YG2+8oYqKCvXp08cugElSaGioduzY4dDiAAAAAKCpcSiExcTE6MCBA7WO/fe//1VMTIwj7QEAAACgyXEohFmt1jrHKisr5eTk5Eh7AAAAAGhy6v1M2OHDh3Xo0CHbvz/99FNVVlba1VRUVGjFihXq1KmTwwsEAAAAgKak3iHs9ddfV2Jiokwmk0wmk2JjY2vUXN4hS01NdXyFAAAAANCE1DuEjR8/XgEBAbJarRo/frySk5NrvAXRbDYrICBAvr6+DbVOAAAAAGgS6h3C7rvvPt13332SLu2KjRo1Sp6eng2+MAAAAABoihz6Ttjjjz/eUOsAAAAAgDuCQyFMkv71r3/prbfe0ldffaWKigq7MZPJpIKCAkenAAAAAIAmw6EQ9vrrr2vatGny8PBQ9+7dZTab7cav9Qp7AAAAALgTORTCXnnlFY0fP16rV6+uEcAAAAAAADU59LHmr776StOnT/9JA9hrr70mk8kkd3f3GmO7du3SkCFD5O7urtatWysqKkoHDx6stU9aWpr8/f1lNpvl5+enxMREVVVV1aizWCyaMmWKvLy85ObmptDQ0DpvqczPz1doaKjc3Nzk5eWlKVOmyGKx1KirqqpSYmKifH19ZTab5e/vr7S0tHpeCQAAAABNgUMh7L777tOxY8caai01HDlyRLNnz1bHjh1rjO3Zs0fh4eE6f/683nnnHa1atUr79u1TWFiYysrK7GqTkpIUHx+vqKgo5eXlKTY2VsnJyYqLi7OrO3funAYPHqyCggKlpqYqOztb7dq1U0REhAoLC+1qCwsLNWLECLVr107Z2dlKTU1Vfn6+Bg8erHPnztnVxsbG6uWXX1ZcXJzy8vIUGRmp+Ph4JScnN9CVAgAAAHC7MFkdeHArLy9Ps2fPVm5urnx8fBpyXZKk0aNHy2QyycPDQ+vWrdPp06dtY+PHj9fWrVt14MABtWrVStKlnblu3brpmWee0ZIlSyRJ5eXluvvuuzV58mS9+uqrtvOTk5O1YMECFRcX6/7775ckpaenKy4uTtu3b1doaKgkqbq6Wr169ZK7u7t27txpOz8kJERnzpzR559/LmfnS3d1bt++Xf3791d6erpmzJghSSopKVFgYKCSkpL07LPP2s5/8sknlZmZqW+++UYeHh43fE1KSkoUEBCg4uJi9ezZs17XE7ge34RNt3oJd5xDKSNv9RIAAIADbub3c4d2wpYvX67vvvtO3bt3V//+/fXLX/7S7mfMmDE33TszM1OFhYVKT0+vMVZdXa2NGzdq3LhxtgAmSZ07d9bAgQOVlbpek1EAACAASURBVJVlO5abm6vKykrFxMTY9YiJiZHVatWGDRtsx7KystSjRw9bAJMkZ2dnTZw4UR9//LGOHDki6dIOXVFRkSZNmmQLYJLUr18/de/e3W7+DRs2yGq11jp/RUWFcnNz63tpAAAAANzGHHoxxxdffCEnJyd5e3vr6NGjOnr0qN24yWS6qb4Wi0WzZs1SSkqK7r777hrjBw4cUEVFhYKCgmqMBQUF6f3331dlZaVcXFxUXFwsSQoMDLSr69Chg7y8vGzjklRcXKywsLBae0qXUq6Pj4/tnLrm/+ijj+x6tm3bVu3bt6+155XzX81isdS4tXL//v111gMAAABo/BwKYYcOHWqgZdiLjY1Vjx49bLf0Xa28vFySar2Nz8PDQ1arVSdPnlSHDh1UXl4us9msFi1a1Fp7udflvnX1vHLe681/Iz1btGih5s2b29VeLT09XYmJiXWOAwAAALj9OPyx5ob2j3/8Q++9954+/fTT6+6kXWv8yrEbrWuoWkd6Xik2NlbR0dF2x/bv36+xY8fWeQ4AAACAxs3hEHbu3DllZGRo27ZtOn78uNLT09WtWzdlZ2crMDBQ99577w33On36tOLi4jRz5kx17NhRp06dkiSdP39eknTq1Ck1a9ZMnp6eklTrLtKJEydkMpnUunVrSZKnp6cqKyt19uxZubm51ah98MEHbf/29PSss6f0487X9ea/cufL09NTn332WY26M2fO6Pz589d8KYe3t7e8vb3rHAcAAABw+3HoxRzHjx/XQw89pBkzZqiwsFAffPCBfvjhB0mXXkixbNmyevc7duyY/vCHP6hNmza2nzVr1ujMmTNq06aNHnvsMXXp0kWurq7avXt3jR67d+9W165d5eLiIunHZ8Guri0tLdXx48cVEBBgOxYYGFhnT0m22st/1lV7dc+ysjKVlpZesycAAACAO4NDIWzu3Lk6deqUPvnkEx0+fFhXvu1+4MCBNb6tdT3t27fX1q1ba/wMHz5cLi4u2rp1qxYvXixnZ2eNHj1a69evt4U+STp8+LC2bt2qqKgo27GIiAi5uLgoIyPDbq6MjAyZTCa7W/siIyO1Z88eu1fRV1dXKzMzU3379rV9r8zHx0chISHKzMzUhQsXbLU7duzQ3r177eYfM2aMTCaTVq9eXWN+V1dXRURE1OsaAQAAALi9OXQ74saNG7VkyRL16dPHLoxI0t13361vvvmmXv1cXFwUHh5e43hGRoacnJzsxhITExUcHKxRo0YpISFBlZWVWrhwoby8vPT73//eVufh4aEFCxbo+eefl4eHh4YNG6aioiK98MILmj59uu0bYZI0depULV++XNHR0UpJSZG3t7fS09O1d+9e5efn261pyZIlGjp0qKKjoxUbGyuLxaKEhAQFBATYvY6+Z8+emjZtmhYtWiQnJycFBwdry5YtWrFihRYvXlyvb4QBAAAAuP05FMK+//57de7cudaxqqoqVVdXO9L+mvz9/bVt2zbNmzdPv/rVr+Ts7KxBgwZp2bJlatu2rV3t/Pnz1bJlSy1fvlzLli1T+/btlZCQoPnz59vVmc1mFRQUaO7cuZo5c6bOnj2r3r17a/PmzRowYIBdbXh4uHJycrRw4UKNHj1abm5uGjVqlJYuXSqz2WxXm56eLh8fH6Wlpam0tFS+vr5KTU3VzJkzf5qLAwAAAKDRMlmvvIewnu677z5NnDhR8+fP14ULF9SsWTN98skn6tOnj1566SVlZWVp165dDbneO97NfJEbuFG+CZtu9RLuOIdSRt7qJQAAAAfczO/nDj0T9thjj2nJkiXKzs62PQ9mMplUVFSk1NRUTZo0yZH2AAAAANDkOHQ74rx58/TRRx8pMjJSbdq0kSQNHz5c5eXlioiIUHx8fIMsEgAAAACaCodCWLNmzZSTk6O1a9dq06ZNOnbsmLy8vDRq1ChNmDBBd93l0EYbAAAAADQ5Dn+s2WQyacKECZowYUJDrAcAAAAAmjS2qgAAAADAQPXeCRs0aJDS09Pl7++vQYMGXbPWZDKpoKDgphcHAAAAAE1NvUPYlW+0v3jxokwm0w3VAgAAAABuIoRt3brV9vdt27Y15FoAAAAAoMnjmTAAAAAAMJBDIWzjxo3685//XOvY8uXLlZOT40h7AAAAAGhyHAphSUlJOn36dK1jZ86cUXJysiPtAQAAAKDJcSiE7dmzR3369Kl17IEHHtC///1vR9oDAAAAQJPjUAg7d+6czp8/X+dYRUWFI+0BAAAAoMlxKIT16NFDGzdurHVs48aN6t69uyPtAQAAAKDJcSiETZ06Va+99poWLVqkY8eOSZKOHTumF154Qa+99pqmTZvWIIsEAAAAgKai3t8Ju9Jvf/tbFRUV6aWXXtLixYvl5OSkCxcuyGq1atKkSfrd737XUOsEAAAAgCbBoRBmMpn0xhtv6IknnlBubq7KysrUtm1bjRgxQo888khDrREAAAAAmgyHQthlYWFhCgsLa4hWAAAAANCkOfRMGAAAAACgfuq9E3bvvfcqKytLvXr1kp+fn0wmU521JpNJBw4ccGiBAAAAANCU1DuEDRgwQK1atbL9/VohDAAAAABgr94hLDU1VS1btpQkZWRkNPR6AAAAAKBJq/czYW3atFFRUZGkS98J++9//9vgiwIAAACApqreIczZ2VkXLlyQdGknrKysrMEXBQAAAABNVb1vR+zUqZNWr16tZs2aSZL27t0rZ+e62/Tp0+fmVwcAAAAATUy9Q9jvfvc7xcfH629/+5tMJpOmTJlSa53VapXJZLLtmgEAAAAAbiKEzZw5Uz//+c9VXFysSZMmacGCBerSpctPsTYAAAAAaHLqHcK++OIL9ejRQ7169dJrr72m3/zmN/L39/8p1gYAAAAATU69X8zxwAMP6IsvvpAkvhEGAAAAAPVU7xBmNpt1/vx5SdK2bdv0/fffN/iiAAAAAKCpqvftiPfee6/+8Ic/qLS0VNKlIPbNN9/UWR8VFXXzqwMAAACAJqbeIez555/X5MmTlZ2dLZPJpISEhDpreTsiAAAAANirdwj79a9/rcGDB2vv3r0KCwvT8uXLdf/99/8UawMAAACAJqfeIUySvLy85OXlpccff1wRERHy8/Nr6HUBAAAAQJN0UyHsstdff93294qKCp04cULt2rWTs7NDbQEAAACgyar32xGvtnXrVoWGhqply5bq3Lmz7fX1cXFxWr9+vcMLBAAAAICmxKEQ9sEHH2jYsGGqrKzU7NmzdfHiRduYl5eXMjIyHF0fAAAAADQpDoWwhQsX6he/+IU+/fRTLV682G6sV69e+uyzzxxaHAAAAAA0NQ49vPXpp5/q3XfflXTpdfRXatu2rSwWiyPtAQAAAKDJcWgnzNnZWVVVVbWOWSwWtWzZ0pH2AAAAANDkOBTCgoOD9eabb9Y6tm7dOoWGhjrSHgAAAACaHIduR0xISNDw4cMVGRmpyZMny2QyaefOnVq1apXWrVunrVu3NtQ6AQAAAKBJcCiEDRkyRKtXr9asWbOUnZ0t6dKr6Vu3bq2MjAw98sgjDbJIAAAAAGgqHP6q8sSJEzVu3Dh99NFHslgs8vLyUv/+/dWiRYuGWB8AAAAANCkOhzBJcnV11ZAhQxqiFQAAAAA0aQ6HsBMnTuiPf/yjCgoKVF5eLi8vLw0ZMkSzZs1SmzZtGmKNAAAAANBkOPR2xCNHjqhPnz5KSkrSd999p06dOunUqVN66aWX1KdPHx09erSh1gkAAAAATYJDIey5555TRUWFdu7cqZKSEr3//vsqKSnRzp07VVFRoeeee66h1gkAAAAATYJDISw3N1eLFy9WcHCw3fHg4GC9+OKL2rx5s0OLAwAAAICmxqEQ9t1338nX17fWMT8/P3333XeOtAcAAACAJsehEObn56dNmzbVOrZ582b5+fk50h4AAAAAmhyH3o4YExOjhIQEXbx4UY8//rg6dOigb7/9VpmZmUpLS1NKSkpDrRMAAAAAmgSHQticOXN04MAB/fnPf9by5cttx61Wq5588knNnj3b4QUCAAAAQFPiUAgzmUx69dVX9f/+3//T1q1bVV5eLk9PTw0aNEjdu3dvqDUCAAAAQJNR72fCTp48qXHjxmnjxo22Yz169NDTTz+t+fPn6+mnn9a+ffs0btw4lZeX16v3Z599ppEjR6pTp05ydXWVh4eHQkNDlZmZWaN2165dGjJkiNzd3dW6dWtFRUXp4MGDtfZNS0uTv7+/zGaz/Pz8lJiYqKqqqhp1FotFU6ZMkZeXl9zc3BQaGqqCgoJae+bn5ys0NFRubm7y8vLSlClTZLFYatRVVVUpMTFRvr6+MpvN8vf3V1paWr2uCwAAAICmo94h7LXXXtPnn3+uiIiIOmsiIiK0e/duu1sUb8SpU6d0zz33KDk5WTk5OXrjjTfk6+urSZMmafHixba6PXv2KDw8XOfPn9c777yjVatWad++fQoLC1NZWZldz6SkJMXHxysqKkp5eXmKjY1VcnKy4uLi7OrOnTunwYMHq6CgQKmpqcrOzla7du0UERGhwsJCu9rCwkKNGDFC7dq1U3Z2tlJTU5Wfn6/Bgwfr3LlzdrWxsbF6+eWXFRcXp7y8PEVGRio+Pl7Jycn1ujYAAAAAmgaT1Wq11ueEBx98UOPHj9e8efOuWbd06VKtXbtWn3zyiUMLlKSHH35YR48e1eHDhyVJ48eP19atW3XgwAG1atVKkvTVV1+pW7dueuaZZ7RkyRJJUnl5ue6++25NnjxZr776qq1fcnKyFixYoOLiYt1///2SpPT0dMXFxWn79u0KDQ2VJFVXV6tXr15yd3fXzp07beeHhITozJkz+vzzz+XsfOmOzu3bt6t///5KT0/XjBkzJEklJSUKDAxUUlKSnn32Wdv5Tz75pDIzM/XNN9/Iw8OjXteipKREAQEBKi4uVs+ePet1LnA9vgm1v+0UP51DKSNv9RIAAIADbub383rvhO3bt08PPfTQdev69Omjffv21bd9rby8vGxhp7q6Whs3btS4ceNsAUySOnfurIEDByorK8t2LDc3V5WVlYqJibHrFxMTI6vVqg0bNtiOZWVlqUePHrYAJknOzs6aOHGiPv74Yx05ckSSdOTIERUVFWnSpEm2NUlSv3791L17d7v5N2zYIKvVWuv8FRUVys3NdeSyAAAAALgN1fvFHNXV1WrWrNl165o1a1brc1c34uLFi7p48aJOnjypd999V3l5efrzn/8sSTpw4IAqKioUFBRU47ygoCC9//77qqyslIuLi4qLiyVJgYGBdnUdOnSQl5eXbVySiouLFRYWVmtP6VLC9fHxsZ1T1/wfffSRXc+2bduqffv2tfa8cv7aWCyWGrdX7t+//5rnAAAAAGjc6h3COnTooH//+9/6+c9/fs26kpKSGuHjRsXGxtpuH2zevLn+53/+R0899ZQk2V72UdttfB4eHrJarTp58qQ6dOig8vJymc1mtWjRotbaK18cUl5eXmfPK+e93vw30rNFixZq3rz5dV9ckp6ersTExGvWAAAAALi91Pt2xAEDBig9Pf2au1xVVVX6y1/+ooEDB97Uop577jkVFRVp06ZNmjp1qn77299q2bJldjUmk6nO868cu9G6hqp1pOfVYmNjVVxcbPdz5S2UAAAAAG4/9d4Je+aZZ/TQQw8pMjJSK1asUMeOHe3Gjx49qieeeEJ79+7V3//+95taVKdOndSpUydJ0i9+8QtJ0rPPPqvHH39cnp6eklTrLtKJEydkMpnUunVrSZKnp6cqKyt19uxZubm51ah98MEHbf/29PSss6f0487X9ea/cufL09NTn332WY26M2fO6Pz589d9KYe3t7e8vb2vWQMAAADg9lLvnbCgoCAtX75ceXl58vPzU79+/fTYY4/pscceU79+/eTn56ctW7Zo+fLlNZ7FulkhISGqrq7WwYMH1aVLF7m6umr37t016nbv3q2uXbvKxcVF0o/Pgl1dW1paquPHjysgIMB2LDAwsM6ekmy1l/+sq/bqnmVlZSotLb1mTwAAAAB3jnqHMEl64okn9M9//lPDhg3TF198oTVr1mjNmjX64osvFBERoQ8//FDTp09vsEVu3bpVd911l+699145Oztr9OjRWr9+vX744QdbzeHDh7V161ZFRUXZjkVERMjFxUUZGRl2/TIyMmQymTR27FjbscjISO3Zs8fuVfTV1dXKzMxU3759bTt+Pj4+CgkJUWZmpi5cuGCr3bFjh/bu3Ws3/5gxY2QymbR69eoa87u6ul7zW2sAAAAAmqZ63454WWhoqN577z1dvHhRx48fl3TpVfJ33XVTuU7Spe9ntWrVSiEhIWrXrp2OHz+ud999V2vXrtWcOXPUtm1bSVJiYqKCg4M1atQoJSQkqLKyUgsXLpSXl5d+//vf2/p5eHhowYIFev755+Xh4aFhw4apqKhIL7zwgqZPn277RpgkTZ06VcuXL1d0dLRSUlLk7e2t9PR07d27V/n5+XbrXLJkiYYOHaro6GjFxsbKYrEoISFBAQEBdq+j79mzp6ZNm6ZFixbJyclJwcHB2rJli1asWKHFixfX+xthAAAAAG5/Nx3CLrvrrrsa7Lml0NBQvf7661q9erVOnTold3d39erVS2+++aYmTpxoq/P399e2bds0b948/epXv5Kzs7MGDRqkZcuW2YLaZfPnz1fLli21fPlyLVu2TO3bt1dCQoLmz59vV2c2m1VQUKC5c+dq5syZOnv2rHr37q3NmzdrwIABdrXh4eHKycnRwoULNXr0aLm5uWnUqFFaunSpzGazXW16erp8fHyUlpam0tJS+fr6KjU1VTNnzmyQawYAAADg9mKyWq3WW70I3Lib+SI3cKN8Ezbd6iXccQ6ljLzVSwAAAA64md/Pb/7eQQAAAABAvRHCAAAAAMBAhDAAAAAAMBAhDAAAAAAMRAgDAAAAAAMRwgAAAADAQIQwAAAAADAQIQwAAAAADEQIAwAAAAADEcIAAAAAwECEMAAAAAAwECEMAAAAAAxECAMAAAAAAxHCAAAAAMBAhDAAAAAAMBAhDAAAAAAMRAgDAAAAAAMRwgAAAADAQIQwAAAAADAQIQwAAAAADEQIAwAAAAADEcIAAAAAwECEMAAAAAAwECEMAAAAAAxECAMAAAAAAxHCAAAAAMBAhDAAAAAAMBAhDAAAAAAMRAgDAAAAAAMRwgAAAADAQIQwAAAAADAQIQwAAAAADEQIAwAAAAADEcIAAAAAwECEMAAAAAAwECEMAAAAAAxECAMAAAAAAxHCAAAAAMBAhDAAAAAAMBAhDAAAAAAMRAgDAAAAAAMRwgAAAADAQIQwAAAAADAQIQwAAAAADEQIAwAAAAADEcIAAAAAwECEMAAAAAAwECEMAAAAAAxECAMAAAAAAxHCAAAAAMBAhDAAAAAAMBAhDAAAAAAMRAgDAAAAAAMRwgAAAADAQI0qhH3wwQeaOnWq/P391aJFC/n4+GjMmDH6v//7vxq1u3bt0pAhQ+Tu7q7WrVsrKipKBw8erLVvWlqa/P39ZTab5efnp8TERFVVVdWos1gsmjJliry8vOTm5qbQ0FAVFBTU2jM/P1+hoaFyc3OTl5eXpkyZIovFUqOuqqpKiYmJ8vX1ldlslr+/v9LS0up5ZQAAAAA0FY0qhP3lL3/RoUOHFB8fr5ycHKWmpspisejhhx/WBx98YKvbs2ePwsPDdf78eb3zzjtatWqV9u3bp7CwMJWVldn1TEpKUnx8vKKiopSXl6fY2FglJycrLi7Oru7cuXMaPHiwCgoKlJqaquzsbLVr104REREqLCy0qy0sLNSIESPUrl07ZWdnKzU1Vfn5+Ro8eLDOnTtnVxsbG6uXX35ZcXFxysvLU2RkpOLj45WcnNzAVw8AAADA7cBktVqtt3oRl1ksFnl7e9sdO336tLp27aqAgADl5+dLksaPH6+tW7fqwIEDatWqlSTpq6++Urdu3fTMM89oyZIlkqTy8nLdfffdmjx5sl599VVbz+TkZC1YsEDFxcW6//77JUnp6emKi4vT9u3bFRoaKkmqrq5Wr1695O7urp07d9rODwkJ0ZkzZ/T555/L2dlZkrR9+3b1799f6enpmjFjhiSppKREgYGBSkpK0rPPPms7/8knn1RmZqa++eYbeXh41OsalZSUKCAgQMXFxerZs2e9zgWuxzdh061ewh3nUMrIW70EAADggJv5/bxR7YRdHcAkyd3dXffff7++/vprSZeC0caNGzVu3DhbAJOkzp07a+DAgcrKyrIdy83NVWVlpWJiYux6xsTEyGq1asOGDbZjWVlZ6tGjhy2ASZKzs7MmTpyojz/+WEeOHJEkHTlyREVFRZo0aZItgElSv3791L17d7v5N2zYIKvVWuv8FRUVys3Nrdf1AQAAAHD7c75+ya313XffadeuXRo0aJAk6cCBA6qoqFBQUFCN2qCgIL3//vuqrKyUi4uLiouLJUmBgYF2dR06dJCXl5dtXJKKi4sVFhZWa0/pUsL18fGxnVPX/B999JFdz7Zt26p9+/a19rxy/tpYLJYat1fu37//mucAAAAAaNwafQiLi4vTmTNnNH/+fEmXbjGUVOttfB4eHrJarfr/7d17UNV1/sfx11lArnk5ICqai4nmBZUyr5u/0NIwcAWTTc3KC1sr6uqUF1JESGlp1d01k1o1LxNG3m01L2lrzrZ5m1p3wFuRpo4uclFLEFGS3x8OJ4+AIejnAD4fM4zT5/P+fr7vc74zycvv7cKFC2rSpIlyc3Pl6uoqT0/PMmtL1ipZt7w1b97vL+2/Imt6enqqTp06drVlSU5OVkJCwm1rAAAAANQs1TqEzZgxQytXrtSCBQvUuXNnuzmLxVLudjfPVbTubtVWZc1bRUdHKzIy0m4sIyND4eHht90OAAAAQPVVbUNYQkKCZs+ercTERI0bN8427u3tLUllnkU6f/68LBaL6tevb6u9cuWKLl++LA8Pj1K1Nwc7b2/vcteUfj7z9Uv7v/nMl7e3tw4ePFiqLj8/X1evXv3Fh3L4+vqWeZ8cAAAAgJqrWj2Yo0RCQoLi4+MVHx+vadOm2c21bNlS7u7uSktLK7VdWlqaAgIC5ObmJunne8Furc3MzFROTo4CAwNtYx06dCh3TUm22pI/y6u9dc3s7GxlZmbedk0AAAAA949qF8JmzZql+Ph4xcbGaubMmaXmnZ2dNWDAAK1fv16XLl2yjZ86dUq7du3SoEGDbGMhISFyc3PT8uXL7dZYvny5LBaL3WV9EREROnr0qN2j6IuKipSSkqJu3brJz89PktS0aVN17dpVKSkp+umnn2y1e/fu1bFjx+z2P3DgQFksFq1YsaLU/t3d3RUSEnKH3w4AAACAmq5aXY44b948xcXFKSQkRKGhodq7d6/dfPfu3SXdOFPWpUsXhYWFKSYmRleuXFFcXJx8fHz02muv2eqtVqtiY2M1Y8YMWa1W9evXTwcOHFB8fLyioqJs7wiTpFGjRmnhwoWKjIxUUlKSfH19lZycrGPHjtneT1birbfeUt++fRUZGano6GhlZWUpJiZGgYGBdo+jb9++vUaPHq2ZM2fKyclJXbp00aeffqpFixZp9uzZd/yOMAAAAAA1X7UKYZs2bZJ04/1eZb1Dq+S90m3atNHnn3+uqVOnavDgwXJ2dlafPn00d+5cNWzY0G6b6dOn64EHHtDChQs1d+5cNW7cWDExMbanLZZwdXXVZ599pilTpmj8+PG6fPmygoKCtHXrVj3xxBN2tcHBwdqyZYvi4uI0YMAAeXh4KCwsTHPmzJGrq6tdbXJyspo2baoFCxYoMzNT/v7+mj9/vsaPH1/l7wsAAABAzWMpLkk2qBEq80ZuoKL8Yz5xdAv3ne+TQh3dAgAAqILK/H5e7e4JAwAAAIDajBAGAAAAAAYRwgAAAADAIEIYAAAAABhECAMAAAAAgwhhAAAAAGAQIQwAAAAADCKEAQAAAIBBhDAAAAAAMIgQBgAAAAAGEcIAAAAAwCBCGAAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQwAAAAADCIEAYAAAAABhHCAAAAAMAgQhgAAAAAGEQIAwAAAACDCGEAAAAAYBAhDAAAAAAMIoQBAAAAgEGEMAAAAAAwiBAGAAAAAAYRwgAAAADAIEIYAAAAABhECAMAAAAAgwhhAAAAAGAQIQwAAAAADCKEAQAAAIBBhDAAAAAAMIgQBgAAAAAGEcIAAAAAwCBCGAAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQwAAAAADCIEAYAAAAABhHCAAAAAMAgQhgAAAAAGEQIAwAAAACDCGEAAAAAYBAhDAAAAAAMIoQBAAAAgEGEMAAAAAAwiBAGAAAAAAYRwgAAAADAIEIYAAAAABhECAMAAAAAgwhhAAAAAGAQIQwAAAAADKp2IezSpUuaMmWK+vXrp4YNG8pisSg+Pr7M2q+//lpPPfWUvLy8VL9+fQ0aNEjHjx8vs3bBggVq06aNXF1d1aJFCyUkJOjatWul6rKysjRixAj5+PjIw8NDPXr00GeffVbmmjt37lSPHj3k4eEhHx8fjRgxQllZWaXqrl27poSEBPn7+8vV1VVt2rTRggULKv6lAAAAAKg1ql0Iy83N1aJFi1RYWKjw8PBy644eParg4GBdvXpVq1ev1tKlS/XNN9+oV69eys7OtqtNTEzUhAkTNGjQIG3fvl3R0dF68803NXbsWLu6wsJCPfnkk/rss880f/58ffzxx2rUqJFCQkK0e/duu9rdu3erf//+atSokT7++GPNnz9fO3fu1JNPPqnCwkK72ujoaP3pT3/S2LFjtX37dkVERGjChAl68803q/htAQAAAKhpnB3dwK1+/etf68KFC7JYLMrJydGSMoRyhAAAF39JREFUJUvKrIuLi5Orq6s2b96sunXrSpI6d+6sVq1aae7cuXrrrbck3Qh1s2fP1u9//3tb6AkODta1a9cUGxuriRMnql27dpKk999/X+np6fryyy/Vo0cPSVLv3r3VqVMnTZkyRfv27bPtf/LkyWrdurXWrl0rZ+cbX2OLFi30m9/8RkuXLtWYMWMkSYcOHdL777+vxMRETZ482bb/kr7+8Ic/yGq13u2vEQAAAEA1Ve3OhFksFlksltvWFBUVafPmzXr22WdtAUy6EeB69+6tDRs22Ma2bdumK1euaOTIkXZrjBw5UsXFxdq4caNtbMOGDXr44YdtAUySnJ2dNXz4cO3fv19nzpyRJJ05c0YHDhzQCy+8YAtgktSzZ0+1bt3abv8bN25UcXFxmfsvKCjQtm3bKvK1AAAAAKglqt2ZsIr47rvvVFBQoI4dO5aa69ixo3bs2KErV67Izc1N6enpkqQOHTrY1TVp0kQ+Pj62eUlKT09Xr169ylxTunFWq2nTprZtytv/v//9b7s1GzZsqMaNG5e55s37v1VWVlapSyszMjLKrQcAAABQ/dXIEJabmytJZV7GZ7VaVVxcrAsXLqhJkybKzc2Vq6urPD09y6wtWatk3fLWvHm/v7T/iqzp6empOnXq2NXeKjk5WQkJCeXOAwAAAKh5amQIK3G7yxZvnqto3d2qrcqaN4uOjlZkZKTdWEZGxm0fWAIAAACgequRIczb21uSyjyLdP78eVksFtWvX99We+XKFV2+fFkeHh6lajt37my3bnlrSj+f+fql/d985svb21sHDx4sVZefn6+rV6/e9qEcvr6+8vX1LXceAAAAQM1T7R7MUREtW7aUu7u70tLSSs2lpaUpICBAbm5ukn6+F+zW2szMTOXk5CgwMNA21qFDh3LXlGSrLfmzvNpb18zOzlZmZuZt1wQAAABwf6iRIczZ2VkDBgzQ+vXrdenSJdv4qVOntGvXLg0aNMg2FhISIjc3Ny1fvtxujeXLl8tisdhd2hcREaGjR4/aPYq+qKhIKSkp6tatm/z8/CRJTZs2VdeuXZWSkqKffvrJVrt3714dO3bMbv8DBw6UxWLRihUrSu3f3d1dISEhVfsyAAAAANQo1fJyxK1btyo/P98WsA4fPqy1a9dKkp555hl5eHgoISFBXbp0UVhYmGJiYnTlyhXFxcXJx8dHr732mm0tq9Wq2NhYzZgxQ1arVf369dOBAwcUHx+vqKgo2zvCJGnUqFFauHChIiMjlZSUJF9fXyUnJ+vYsWPauXOnXY9vvfWW+vbtq8jISEVHRysrK0sxMTEKDAy0exx9+/btNXr0aM2cOVNOTk7q0qWLPv30Uy1atEizZ8/mHWEAAADAfcZSXFxc7OgmbuXv76+TJ0+WOXfixAn5+/tLkr766itNnTpVe/bskbOzs/r06aO5c+eqZcuWpbZ7++23tXDhQn3//fdq3LixRo4cqenTp8vFxcWu7ty5c5oyZYo2b96sy5cvKygoSLNmzdJTTz1Vas0dO3YoLi5OBw8elIeHh8LCwjRnzpxS93Fdu3ZNiYmJWrZsmTIzM+Xv769x48Zp/Pjxd/zdHDp0SIGBgUpPT1f79u3veHvgdvxjPnF0C/ed75NCHd0CAACogsr8fl4tQxjKRwjDvUQIM48QBgBAzVaZ389r5D1hAAAAAFBTEcIAAAAAwCBCGAAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQwAAAAADCIEAYAAAAABhHCAAAAAMAgQhgAAAAAGEQIAwAAAACDCGEAAAAAYBAhDAAAAAAMIoQBAAAAgEGEMAAAAAAwiBAGAAAAAAYRwgAAAADAIEIYAAAAABhECAMAAAAAgwhhAAAAAGAQIQwAAAAADCKEAQAAAIBBhDAAAAAAMIgQBgAAAAAGEcIAAAAAwCBCGAAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQwAAAAADCIEAYAAAAABhHCAAAAAMAgQhgAAAAAGEQIAwAAAACDCGEAAAAAYBAhDAAAAAAMIoQBAAAAgEGEMAAAAAAwiBAGAAAAAAYRwgAAAADAIEIYAAAAABhECAMAAAAAgwhhAAAAAGAQIQwAAAAADCKEAQAAAIBBhDAAAAAAMIgQBgAAAAAGEcIAAAAAwCBCGAAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihBmQl5eniRMnys/PT25ubgoKCtJHH33k6LYAAAAAOICzoxu4HwwaNEgHDhxQUlKSWrdurQ8//FBDhw7V9evXNWzYMEe3BwAAAMAgQtg9tmXLFu3YscMWvCSpd+/eOnnypCZPnqznnntOTk5ODu4SAAAAgClcjniPbdiwQV5eXoqMjLQbHzlypM6ePat9+/Y5qDMAAAAAjsCZsHssPT1dbdu2lbOz/VfdsWNH23zPnj3L3DYrK0vZ2dl2YxkZGfemUQAAAABGEMLusdzcXD300EOlxq1Wq22+PMnJyUpISLhnvQG3+j4p1NEtAAAA1HqEMAMsFkul5qKjo0tdxpiRkaHw8PC71hsAAAAAswhh95i3t3eZZ7vOnz8v6eczYmXx9fWVr6/vPesNAAAAgHk8mOMe69Chg44cOaKioiK78bS0NElSYGCgI9oCAAAA4CCEsHssIiJCeXl5Wrdund34ihUr5Ofnp27dujmoMwAAAACOwOWI91j//v3Vt29fjRkzRj/++KMCAgKUmpqqbdu2KSUlhXeEAQAAAPcZQpgB69ev1/Tp0xUXF6fz58+rTZs2Sk1N1ZAhQxzdGgAAAADDCGEGeHl5af78+Zo/f76jWwEAAADgYNwTBgAAAAAGEcIAAAAAwCBCGAAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQwAAAAADCIEAYAAAAABhHCAAAAAMAgQhgAAAAAGEQIAwAAAACDCGEAAAAAYBAhDAAAAAAMIoQBAAAAgEGEMAAAAAAwyNnRDeDOFBYWSpIyMjIc3AkAAACAkt/LS35PrwhCWA1z+vRpSVJ4eLiDOwEAAABQ4vTp03r00UcrVGspLi4uvsf94C66ePGidu/erQcffFCurq6ObqdGysjIUHh4uDZu3KiAgABHt4Mq4njWPhzT2oXjWftwTGsfjmnVFBYW6vTp03riiSdUv379Cm3DmbAapn79+ho4cKCj26gVAgIC1L59e0e3gbuE41n7cExrF45n7cMxrX04ppVX0TNgJXgwBwAAAAAYRAgDAAAAAIMIYQAAAABgkFN8fHy8o5sATPP09FRwcLA8PT0d3QruAo5n7cMxrV04nrUPx7T24ZiaxdMRAQAAAMAgLkcEAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQw3Bfy8vI0ceJE+fn5yc3NTUFBQfroo48c3Raq4NKlS5oyZYr69eunhg0bymKxiIe91lz//Oc/NWrUKLVp00aenp5q2rSpBg4cqK+++srRraESDh48qNDQUDVv3lzu7u6yWq3q0aOHUlJSHN0a7qIlS5bIYrHIy8vL0a2gEj7//HNZLJYyf/bu3evo9mo9Z0c3AJgwaNAgHThwQElJSWrdurU+/PBDDR06VNevX9ewYcMc3R4qITc3V4sWLVKnTp0UHh6uJUuWOLolVMG7776r3NxcTZgwQe3atVN2drbmzZun7t27a/v27erTp4+jW8QduHjxoh588EENHTpUTZs2VX5+vlauXKkXXnhB33//vWJjYx3dIqrozJkzmjRpkvz8/PTDDz84uh1UwZtvvqnevXvbjQUGBjqom/sHj6hHrbdlyxaFhobagleJfv366dChQzp16pScnJwc2CEqo+R/XRaLRTk5OWrYsKFmzpzJ2bAaKisrS76+vnZjeXl5CggIUGBgoHbu3OmgznA3de/eXWfPntWpU6cc3QqqaMCAAbJYLLJarVq7dq3y8vIc3RLu0Oeff67evXtrzZo1Gjx4sKPbue9wOSJqvQ0bNsjLy0uRkZF24yNHjtTZs2e1b98+B3WGqii5ZAK1w60BTJK8vLzUrl07nT592gEd4V7w8fGRszMX4dR0KSkp2r17t5KTkx3dClBjEcJQ66Wnp6tt27al/uLv2LGjbR5A9fPDDz/o66+/Vvv27R3dCirp+vXrKioqUnZ2tpKTk7V9+3ZNnTrV0W2hCrKysjRx4kQlJSWpWbNmjm4Hd8HYsWPl7OysunXr6umnn9YXX3zh6JbuC/xzFGq93NxcPfTQQ6XGrVarbR5A9TN27Fjl5+dr+vTpjm4FlRQdHa2///3vkqQ6dero7bff1iuvvOLgrlAV0dHRevjhhzVmzBhHt4IqqlevniZMmKDg4GB5e3srIyNDc+bMUXBwsD755BM9/fTTjm6xViOE4b5wu8vWuKQNqH5mzJihlStXasGCBercubOj20ElTZs2TVFRUcrKytKmTZs0btw45efna9KkSY5uDZWwbt06bdq0Sf/5z3/4u7MWeOSRR/TII4/Y/rtXr16KiIhQhw4dNGXKFELYPUYIQ63n7e1d5tmu8+fPS/r5jBiA6iEhIUGzZ89WYmKixo0b5+h2UAXNmzdX8+bNJUnPPPOMJOn111/XSy+9pIYNGzqyNdyhvLw8jR07VuPHj5efn58uXrwoSbp69aqkG0/EdHFxkaenpyPbRBXVr19fYWFheu+991RQUCB3d3dHt1RrcU8Yar0OHTroyJEjKioqshtPS0uTxGNYgeokISFB8fHxio+P17Rp0xzdDu6yrl27qqioSMePH3d0K7hDOTk5OnfunObNm6cGDRrYflJTU5Wfn68GDRro+eefd3SbuAtufvow7h3OhKHWi4iI0OLFi7Vu3To999xztvEVK1bIz89P3bp1c2B3AErMmjVL8fHxio2N1cyZMx3dDu6BXbt26Ve/+lWZ9+miemvcuLF27dpVajwpKUm7d+/W1q1b5ePj44DOcDdduHBBmzdvVlBQkNzc3BzdTq1GCEOt179/f/Xt21djxozRjz/+qICAAKWmpmrbtm1KSUnhHWE12NatW5Wfn69Lly5Jkg4fPqy1a9dKunHpk4eHhyPbwx2YN2+e4uLiFBISotDQUO3du9duvnv37g7qDJXx8ssvq27duuratasaNWqknJwcrVmzRqtWrdLkyZO5FLEGcnNzU3BwcKnx5cuXy8nJqcw5VG/Dhg1T8+bN9dhjj8nHx0fffvut5s2bp3Pnzmn58uWObq/W42XNuC/k5eVp+vTpWr16tc6fP682bdro9ddf15AhQxzdGqrA399fJ0+eLHPuxIkT8vf3N9sQKi04OFi7d+8ud56/qmqWZcuWadmyZTpy5IguXrwoLy8vderUSVFRURo+fLij28NdNGLECF7WXEMlJSVp1apVOnHihPLy8mS1WvX444/r9ddfV5cuXRzdXq1HCAMAAAAAg3gwBwAAAAAYRAgDAAAAAIMIYQAAAABgECEMAAAAAAwihAEAAACAQYQwAAAAADCIEAYAAAAABhHCAAAAAMAgQhgAAAAAGEQIAwDUahEREXJ3d9fFixfLrXn++efl4uKic+fO3ZV9NmvWTFFRUXe8XVFRkSwWiyZOnPiLtTt37pTFYtEXX3xRmRYBAA5ECAMA1GqjR4/WlStX9OGHH5Y5/8MPP2jDhg0KCwtTo0aN7so+N23apGnTpt2VtQAAtQ8hDABQq/Xv319+fn5aunRpmfOpqakqKCjQ6NGjq7yvgoICSdIjjzyihx56qMrrAQBqJ0IYAKBWc3Jy0ksvvaSvvvpKaWlppeaXLVumJk2aqH///pKkuLg4de3aVVarVXXr1lXnzp21fPlyFRcX223XrFkzhYeHa82aNQoKCpKbm5sSExNtczdfjlhQUKBXX31VnTp1Ur169WS1WtWzZ09t2rSp3L7fffddtWrVSq6urmrfvr3WrFlToc+7f/9+hYWFqUGDBnJzc9Ojjz6qdevW2dXk5+fr1VdfVYsWLeTm5iar1aouXbpo9erVFdoHAKBqnB3dAAAA99qoUaOUlJSkpUuX6q9//att/PDhw9q/f79iYmLk5OQkSTp58qTGjBmjBx98UMXFxdq7d6/GjBmjs2fPlrrEcP/+/UpPT1dsbKz8/f3l5eVV5v4LCgp08eJFTZkyRX5+fiosLNSOHTsUHh6uDz74QMOGDbOrX79+verVq6fZs2fL3d1d77zzjp577jk5OzsrIiKi3M+5c+dOhYaGqmfPnlq0aJEeeOABpaamavDgwfrggw80fPhwSdKECRP00UcfKTExUUFBQcrLy1NaWppyc3Mr9f0CAO6MpfjWf9oDAKAWCg4O1qFDh3T27Fm5uLhIkiZNmqR58+bpm2++UatWrUptc/36dV2/fl2zZs3Se++9Z/fgjmbNmikrK0tHjhxRy5Yt7bZr1qyZQkJCtGTJkjJ7+emnn1RcXKyoqChbEJRuPJjDxcVFnp6eOnHihBo2bGirb9u2rZycnHTkyBFJNwJX37599a9//UuPP/64JKlVq1Zq0KCB9uzZYwuV0o1LMtPS0nT69GlZLBa1bdtWgYGBFT67BgC4u7gcEQBwXxg9erRycnL0j3/8Q9KNwJOSkqJevXrZBbCdO3fqySefVL169eTk5CQXFxe98cYbysrKKnWmKCgoqFQAK8+qVavUs2dPeXp6ytnZWS4uLlqxYoUtVN2sb9++tgAm3bik8ne/+52OHj2qzMzMMtc/evSoMjIy9Pzzz6u4uFhFRUW2n2eeeUZnzpxRRkaGJKlr167avHmzpk2bpt27d9vuZQMAmEEIAwDcFwYPHqx69epp2bJlkqQtW7bo3Llzdg/k2LNnj0JCQuTk5KQlS5boyy+/1IEDBxQTEyNJpcJKkyZNKrTv1atXa8iQIWrevLlWrlypPXv26MCBA3rxxRfLDECNGzcud6y8SwZLztJNnDhRLi4udj9//OMfJUk5OTmSpIULF2rSpElat26dgoODZbVaFRERoe+++65CnwcAUDXcEwYAuC+4u7tr6NChWrx4sf73v/9p6dKleuCBBxQZGWmrSU1NlaurqzZv3qw6derYxteuXVvmmhaLpUL7TklJUatWrZSammq3TWFhYZn1ZZ3tKhnz9vYucxsfHx9J0owZM/Tb3/62zJo2bdpIkry8vDRr1izNmjVLmZmZ2rp1q6ZOnaqBAwcqPT29Qp8JAFB5hDAAwH1j9OjReu+99zRnzhxt2bJFI0aMkIeHh23eYrHIxcVFv/rVzxeKXL58WSkpKVXar8ViUZ06dewC2NmzZ7V58+Yy63fs2KHs7Gy7e8JWr16thx9+uMyzZJLUrl07tWjRQgcPHtQbb7xR4d4aN26skSNH6uuvv9Y777yjwsJCubq63sGnAwDcKUIYAOC+8dhjj6ljx47629/+puLi4lLvBgsNDdXbb7+t4cOHKyoqSjk5Ofrzn/9sF9QqIywsTC+//LLGjx+viIgInTp1Sm+88Yb8/Px0/PjxUvVWq1V9+vRRbGysPDw8tGDBAn377bflnpGTbgS9RYsWKTQ0VP3799eLL74oPz8/XbhwQYcPH9Z///tfrVq1yvY9hIeHq0OHDmrQoIEOHTqklStX6v/+7/8IYABgACEMAHBfGT16tCZMmKB27dqpW7dudnP9+vXT4sWLNWfOHIWFhalp06Z65ZVXVL9+fb3yyiuV3mdUVJSys7O1ePFiLV68WC1btlRsbKyOHz+upKSkUvWDBg1SQECApk2bptOnTysgIECpqal69tlnb7ufp556Svv27VNiYqImTJigixcvysfHR+3atdOQIUNsdX369NHGjRv1l7/8RQUFBfLz89PIkSM1ffr0Sn9GAEDF8Yh6AAAAADCIpyMCAAAAgEGEMAAAAAAwiBAGAAAAAAYRwgAAAADAIEIYAAAAABhECAMAAAAAgwhhAAAAAGAQIQwAAAAADCKEAQAAAIBBhDAAAAAAMIgQBgAAAAAGEcIAAAAAwCBCGAAAAAAY9P+ExZksZBU23gAAAABJRU5ErkJggg==\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_2.coef_\n",
    "plt.bar( x, y )\n",
    "plt.xlabel( \"Variables\")\n",
    "plt.ylabel('Coefficients')\n",
    "#plt.title('Normalized Coefficient plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.38855940797147"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_x,test_y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.predict ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Temperature prediction at Node 1 at 2.5s for an object with Diffusion Coeff = 10e-06 for T_left = 400 C and T_right = 100C\n",
    "t = 2.7\n",
    "#lt = np.log(t)\n",
    "DC = 0.00001\n",
    "T_l = 400\n",
    "T_r = 100\n",
    "deltaT = T_l - T_r\n",
    "x   = 0.33\n",
    "deltaTxL = deltaT * x \n",
    "#DCParameter = (DC * t * deltaT) / np.square(x)\n",
    "Timespace1 = np.exp((-10)*t) * (np.square(x)-x) * (T_l + T_r)\n",
    "Timespace2 = np.exp((-40)*t) * (np.square(x)-x) * (T_l - T_r)\n",
    "Timespace3 = np.exp((-90)*t) * (np.square(x)-x) * (T_l + T_r)\n",
    "\n",
    "Predict_Temp = ((lr.intercept_) + (lr.coef_[0])*(T_l) + (lr.coef_[1])*(deltaTxL) + (lr.coef_[2])*(Timespace1) + (lr.coef_[3])*(Timespace2) + (lr.coef_[4])*(Timespace3)) \n",
    "\n",
    "##Predict_Temp = (lr.intercept_) + (lr.coef_[0])*(t_l) + (lr.coef_[1])*(t_r) + (lr.coef_[2])*(nldc) + (lr.coef_[3])*(lt) + (lr.coef_[4])*(n_0) + (lr.coef_[5])*(n_1) + (lr.coef_[6])*(n_2) + (lr.coef_[7])*(n_3) + (lr.coef_[8])*(n_4) \n",
    "\n",
    "print(Predict_Temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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