sckit_learn Model (Feature).ipynb

{
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   "id": "3fa49dab",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nAdvertising Sales data\\nFinding the best fit line using sckit-learn model\\nError anlysis is done by plotting the histogram of the errors\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Advertising Sales data\n",
    "Finding the best fit line using sckit-learn model\n",
    "Error anlysis is done by plotting the histogram of the errors\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d51020fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing \n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import statsmodels.api as sm \n",
    "\n",
    "# supress the warning \n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10af53d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     TV  Radio  Newspaper  Sales\n",
       "0             1  230.1   37.8       69.2   22.1\n",
       "1             2   44.5   39.3       45.1   10.4\n",
       "2             3   17.2   45.9       69.3    9.3\n",
       "3             4  151.5   41.3       58.5   18.5\n",
       "4             5  180.8   10.8       58.4   12.9\n",
       "..          ...    ...    ...        ...    ...\n",
       "195         196   38.2    3.7       13.8    7.6\n",
       "196         197   94.2    4.9        8.1    9.7\n",
       "197         198  177.0    9.3        6.4   12.8\n",
       "198         199  283.6   42.0       66.2   25.5\n",
       "199         200  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "\n",
    "advertising = pd.read_csv('D:Advertising.csv')\n",
    "advertising"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69ddd68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peeking the data\n",
    "\n",
    "advertising.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c4f45e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peeking the data\n",
    "\n",
    "advertising.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3e5bdb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
       "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
       "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
       "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
       "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
       "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
       "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
       "max    200.000000  296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfd37cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x209c5853850>"
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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualisation\n",
    "\n",
    "sns.pairplot(advertising, x_vars = ['TV', 'Radio' , 'Newspaper'] , y_vars = 'Sales', size = 3 , kind = 'scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aad09cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RX/07sYZJQETigeZY18FK5E7zwkfAI1h3o+WWkdYwDMO3KqmfrqrmiMgDwGysLmMfq+o6EbnHXj8GGA58KiJrsOrYT6lqSok7xb1C95Cq/nB64RuGYXhJJd4boaqzsEZXzL9sTL7Xe4FLy7NPdwrdX0TkNWAaBXsvrHDnANXxCQsAZ7T40tchVLq1a6vnuaqO+lxfamWqytoyq+w0ZaoGA96c7CLRKd8yxbpaZxiG4V/K0XvBF9zpvXCRNwIxDMOoFFW1pisijxZapEAK8KuqFvewSsMwDN/z80K3tC5joYWmMKwmhh9E5HovxGYYhlF+jnJMPlBiTVdVXyhuuYhEAf/Dug/ZMAzDv/h5Tdet8XTzU9U0e3AHwzAMv6NV/UJaYSLSCzjogVgMwzBOn5/XCUu7kLaGooM7RGHde3yLJ4MyDMOoMP8uc0ut6V5RaF6BVFU97MF4DMMwTk9VfVyPqu7wZiCGYRiVoqo2LxiGYVRJ/l3mmkLXMIxqJsBHHXDdZApdwzCqFTU1XcMwDC+qqhfSDMMwqiRzIc0wDMOLqmpNV0RGA1+q6m9ejCfP74s2Mvq16eS6XPS7qjM33l5w+F5V5d3/TGfJog0EBdXg6RcGcVbL+gC8OmwyixesJyIqhE+/fiJvmz837eHNEVM5np2D0+ngkWf/Qcs2Db2aL3eNee1u+l7cgeTUDDr1ftLX4ZRq+W8bGfvGdFwuF30GdOa6W4ueq7FvTGfZog3UDKrBo/8exJktrHN1a/8R1AquidPhwBHg4J3PHgZg4rjZzP72d8IjQgD45/19Off8llU+X6888zl7diQDkJV1lJCQWoz+svCAft7T/Zx6PH93Z5wO4b+zNzN2ypoC6+8c2Ib+Pc8AIMDpoGmDcM4bPIlDWce57apWXNfnLFRh0/aDPPXWrxw/4QdP9PLv62il1nT/BN4QkTrAZGCSqiZ6I6jcXBdvj/yG1z8YQmx8OPfc+Dbn92hF46YJeWl+/3Uju3cm88X0p1m/ZidvvTyVDz5/CIDLruzE1YPO5+X/m1Rgv2NHfc+tQ3rT+YKWLFm4gTGjZvL2+Pu8kaVy+3zKfMZMmM34t/wzvpNyc128/59vGDF6CDHx4Tz8z7fp0r0VDc84da6W/7aRPTuTGT/taTat3cnokVMZ9elDeetHjrmX8IjaRfZ91eDuDLy5pzeyUYSn8vXMKzfnvf7wrRnUDgnyfGZK4HAIw+7rwj+fm83+lCNMG3Ulc5fsZMuuQ3lpxk9dy/ipawHodV4Dbru6NYeyjhMfHcwt/Vtx2T3fkH08l3ee6ckVPZow7X9bfJWdU5z+XeqWGJ2qvq2qXYEeQBrwiYhsEJF/ichZngxq49qd1GsQTd360QQGBtCrT3sWzVtXIM2i+evoc0UnRITWZzciK/MYqcnWo+bbndOU0PDgIvsVgcOHrScOHc46RkxsuCezcVoWLd1IWnqWr8Mo0+Z1O6nbIJo69rnq3rs9i+cXPFdL5q/j4n7WuWrRthGHM4+RlpLho4jd4+l8qSoL/7eKHn06eCJ8t7Q7K4YdezPZtT+LEzkuvl+wlUu6lvzL74qeTZg579SDbgOcDoJqOHE6hKCaASSlHvFG2GWqxKcBe0SZXwmqukNVX1XVDsANwNXABk8GlZx0iNj4iLz52PgIkpMPFU2TkD9NOMlJBdMU9sDjAxgzaibXXjacD976jrse7FuZYf8tpSYfIibfuYqJjyC10LlKSS54PmPiwkmxz5UIPP/AOIbe/BY/TFtSYLvvpizivsFv8NaLk8nM8O4ftCfzBbB25VYiokOp1zDWI/G7Iz46mH0pp+7q359yhPjoor84AIJqOul+Tn1+XLQdgAOpRxg/bS0LJlzH4i+uJ/PwcX5dudcbYZetqo6ne5KIBAKXYT3z/WJgPlDsWLueJIVvM9HCY/FAWSNOTp+ymPsf60+PS87mlzmJ/OeFKbw59u7KDPNvp5jTUPQ8lHKuXh//ANGx4aSnZfLcA+Oo3ziWth2b0m9gNwbf0RsR+HzMbMaP+o5H/jXIE1kolqfyddL8OYn0vLR9ZYZcbsX9vWhxGQd6dW7IivUHOJR1HICwkBpc0qUhF902hYzDx3n32YsYcNEZTP9la7Hbe5WfX0grsawXkd4i8jGwGxiC9Rjipqo6SFW/LW2nIjJERJaLyPKJH/9Y7qBi48JJPpCeN598IJ2Y2LCCaeIjSN6fP82hImkKmz1zOd0vbgtAz97t2LhuZ7ljMwqKiQsnJd+5SjmQTlRMWKE0EQXOZ0rSIaLtcxVtN/FERIXStWcbNq/bBUBkdChOpwOHw8FlV3Vms5fPlafyBZCbk8tvv6yhe+/2HovfHftTDlMn5lTNNiEmmKS04n9RXNG9Cd/NP/WUrvPb12X3/kzSMrLJyVVmL9pBx5ZxHo/ZLSLuTz5QWgX7P8BioKWqXqmqX7g7wpiqjlPVTqra6abbLyt3UM1bN2D3zhT27UnlxIkcfp6dSLeerQuk6dajFbNnLkdVWbd6B7VDgvI+8CWJjg0j8Y+/AFixdAv1G8aUOzajoLNaNWDvzhT22+dqwU+JdOle8Fx17t6Kud9b52rjGutcRcWEcexoNkcOHwPg2NFsVi7ZTCP7Ymn+ttHf5q2lUdM63ssUnssXwMqlf1K/UVyB5gtfWL05hUZ1w6gfH0JggIN+3c9g7pJdRdKFBAdyXtsE/rf41Bff3uQs2reIJaimE4Bu7esWuADnU05xf/KBUpsXVPVDbwWSX0CAk4eeupon7vsQl0vpO+BcmjRNYPoUq/fagGu70eWClvz+60Zu7D+SmkGBPDXs1E/PF5+eSOIff3Eo/TDX9BnObfdcSr+rO/P4/13L6Ne+JTfHRY2aATz2/LW+yJ5bJrz7IBd2bUlMZChbfh/N8De/ZsLkeb4OqwhngJN7n7ya54d+iCtXubT/uTRqmsD3U61z1W9gN849vyXLFm3kjqutc3WymeBgahYvPfkpALk5Lnpe1oFO3VoA8NE7M9m6eS8iQnydSB589ppqkS+ABXMS6dGnvVfzU5xcl/LCB0v45KVLcTqEKXP+5M+d6Qy+vDkAk2ZtAuDSbo34dcUejmbn5G27alMKP/66nenv9Cc3V1m/NZXJP2zyST4KUz9vXpCS2nBEZDfwZkkbqmqJ6/Lbd+S74g9QxZ3R4ktfh1Dp1q69wdchGG7qc32Kr0PwiC2zbjvtErPxc7PcLnO2j7jc6yV0aTVdJ9ZTgA3DMKqOKnwb8L6SnghsGIbht/z73ohSw/PvrwvDMIziVGLvBRG5TEQ2icgWEXm6hDQ9RSRRRNaJyPyy9llaTffiMiMyDMPwN5U0iLmIOIH3gN5YXWeXicgMVV2fL00E8D5wmaruFJEy+82Vdhtw2mlHbRiG4WWVeBvwecAWVd2qqseBr4ABhdLcAExT1Z0AqppU1k79vPXDMAyjnCrvNuB6QP6Oy7vtZfmdBUSKyDwR+UNEbilrp2Y8XcMwqpdy9F4QkSFYd9yeNE5Vx51cXcwmhbujBQDnYDXH1gIWi8gSVd1c0jFNoWsYRvVSjpsj7AJ2XAmrdwMN8s3XBwqP6rMbSLHv1j0sIguAdkCJha5pXjAMo3pxiPtT6ZYBzUSkiYjUwBr0a0ahNNOBC0UkQESCgc6UMQqjqekahlGtaCWNqaCqOSLyADAb62axj1V1nYjcY68fo6obRORHYDXgAsar6trS9msKXcMwqpdKvCNNVWdhjbCYf9mYQvOvAa+5u09T6BqGUb34+YA3ptA1DKN68e8y1xS6hmFULw4/7x5gCl3DMKoVU+gahmF4UVnPSvQ1U+gahlGt+HmZ6/lCNyaovqcP4RPV8SkLbdpUv6dhAAyaeI+vQ6h0xy8J8XUIfutvX+gahmF4k5g2XcMwDO8xNV3DMAwvcpqarmEYhveYmq5hGIYXmS5jhmEYXmQupBmGYXiRn1d0TaFrGEb1Ym4DNgzD8CI/H9nRFLqGYVQv1aZ5QUTaARfaswtVdZVnQjIMw6g4fy903Wr9EJGHgC+AOHuaKCIPejIwwzCMihCHuD35grs13TuAzvZjhhGRV4HFwLueCswwDKMi/L2m626hK0Buvvlc/P6hGIZh/B1Vl94LnwC/i8g39vxVwEceicgwDOM0VIveC6r6pojMAy7AquHepqorPRmYYRhGRVTp5gURCVPVDBGJArbb08l1Uaqa5tnwDMMwyqeq3wb8JXAF8Aeg+ZaLPX+Gh+IqQFV55eUJLFywkqCgmox4+V5atW5SNNgvfuTzz35g184DLPxtHJGRYQBs3bqH/3t2DOvXb2Pow4O47fYrvRF2sZb/tpGxb0zH5XLRZ0Bnrru1V4H1qsrYN6azbNEGagbV4NF/D+LMFtbTN27tP4JawTVxOhw4Ahy889nDAEwcN5vZ3/5OeIT1NIF/3t+Xc89v6dV8uWvMa3fT9+IOJKdm0Kn3k74Ox20H165l+1eTUZeL+AsvoF7fvgXWpyUmsuvb6SCCOJ00HnQdYc2aAbDi6WdwBNVExIE4nZz9/HO+yEKxejSOYljPZjgd8NWafby/bGeB9V3qRzB+QFt2HToKwI9bUnh7yXYA7uhYn8Ft6qIoG1MO8/jsjWTnurydhSKqdE1XVa+w/y9awnnRwgWJ7Nyxj1k/jmL1qi0Mf3E8kyaPKJKuQ4fm9OjZkdtuebHA8vDwEJ5+7lZ+nrvMWyEXKzfXxfv/+YYRo4cQEx/Ow/98my7dW9HwjIS8NMt/28iencmMn/Y0m9buZPTIqYz69KG89SPH3Et4RO0i+75qcHcG3tzTG9k4LZ9Pmc+YCbMZ/9Z9vg7Fbepyse3LL2n1yCPUiIxkzYiXiWzXjuC6dfPShLdoQeS/2yEiHN69m81jx9Jh+PC89a0fe4zA0FBfhF8ih8BLvc7ixqmJ7MvM5rsbO/HTXyn8mXakQLple9K57ds1BZbFh9Tgtg71uXjCUrJzXLzfrzVXNo/j6/X7vZmFYvn7KGOlVsRFpGNpk7eC/OXn5fQf0B0RoV37ZmRmHCE56WCRdC1bNaFevbgiy6Ojw2nbtikBAU5vhFuizet2UrdBNHXqRxMYGED33u1ZPH9dgTRL5q/j4n6dEBFatG3E4cxjpKVk+Cjiyrdo6UbS0rN8HUa5ZG3bRlBsHEGxsTgCAog591wOJha8N8gZFJT3x+7KzkaqQOee9glhbE8/ys5DxzjhUr7beIBLm8a4vX2AQwgKcOAUoVaggwOHsz0YrfscDvcnXyireeEN+/8goBOwCqtp4Wzgd6wLax534EAaCQnRefPxCVEcSEojNi7SG4evNKnJh4iJj8ibj4mPYNPaHQXSpCQfIjZ/mrhwUpIOERUThgg8/8A4RKDv1V3p+48ueem+m7KIubP+oFnL+tz58JWEhgV7Ojt/G8fT06kZFZU3XyMygsxt24qkS12xkp3fTONERiYthxa8d2jDqFGAEN+jO/Hdu3s4YvckhNRkb+axvPl9Wdm0rxNWJF3HOuH8ePO5HMjKZsSCLWxOPcKBrOOMW76LJXd25ViOiwU70li4o2hFyBf8vKJbZvPCRQAi8hUwRFXX2PNtgMdL2k5EhgBDAN7/4DnuHDLwtIJULbrM339CFMetfBST6GSa18c/QHRsOOlpmTz3wDjqN46lbcem9BvYjcF39EYEPh8zm/GjvuORfw3yRBb+noo7J8XUZKM7diC6YwcyNm9m1/TptHr0UQDaPP0UNSIiOJGRwfq3RlErIYGws87yeNhlKe4vqHBW1yZl0nX8Yo6cyOWiJlF82L8tPT75nfCaAfRuGsP5Hy0hIzuHD65ozdUt4/lmwwGvxF4af+8y5m4Fu8XJAhdAVdcC7UtKrKrjVLWTqnaqaIE76YvZDLz6KQZe/RRxcZHs35+at+7A/jTiYqtWLRfsWuuB9Lz5lAPpRMWEFUoTQXL+NEmHiI610kTHhgMQERVK155t2LxuFwCR0aE4nQ4cDgeXXdWZzesKXgwxTk+NyEiy00511Dl+MJ0aERElpg876yyOJSVzIjPT2t5OGxgWRlSH9mRt2+7BaN23LyubuqFBefN1QmqSlFWwiSDreC5HTlj3Rf2yLY0AhxAZFMgFDSPZlXGUtKMnyHEpP/6ZzDl1wr0af0kc4v5UFhG5TEQ2icgWEXm6lHTnikiuiFxTZnxu5mODiIwXkZ4i0kNEPgQ2uLlthQy+sQ9Tv3mVqd+8Sq+LOzFj+gJUlVWJfxISGlzlmhYAzmrVgL07U9i/J5UTJ3JY8FMiXbq3LpCmc/dWzP1+OarKxjU7qB0SRFRMGMeOZnPksPVT8NjRbFYu2UyjptYFuPxtvr/NW0ujpnW8l6m/gZDGjTmWlMSx5BRcOTmkLFtGZLt2BdIcTUpC7Wpi1o4duHJzCQgJITc7m9xj1nnLzc4mff16atWrW+QYvrBqfyZNImrRICyIQIdwZYt4ftqaUiBNbHCNvNftEkJxiHDw2An2ZGbTMSGcoACrCDm/YSRb0g57Nf6SOETdnkojIk7gPaAv0AoYLCKtSkj3KjDbnfjcvSPtNuBe4ORl9AXAB25ue9q69+jAwgWJ9O3zELWCajL85Xvy1t07ZCQvvDSEuLgoJn7+A5989B0pKen8Y8BTXNi9PS++dDcpyekMuvZZsrKO4nAIEz/7gekzXyckxLvtns4AJ/c+eTXPD/0QV65yaf9zadQ0ge+n/gZAv4HdOPf8lixbtJE7rh5JzaDAvGaCg6lZvPTkpwDk5rjoeVkHOnVrAcBH78xk6+a9iAjxdSJ58Nkyv2x9ZsK7D3Jh15bERIay5ffRDH/zayZMnufrsEolTidNbhjMhlGjUHURd/75BNery/558wFI6NmDtD9WkLx4MeJ04qhRg7OG3IWIcCIjg03vW38qmptLTOfziGzTxpfZyZOryv/9spnPB7bDKcLktfvYnHqEm862vhQmrt7L5WfFcvPZ9chR5VhOLg98b134Tdyfwaw/k5h1UydyXcq6pCy+XLPXl9nJE1B5zQvnAVtUdSvkNbMOANYXSvcgMBU4152dihbX0FiJTrhWevYAPrIza7evQ6h0bdp86esQPGLQxHvKTlTF/Lzc9/1hPWHnoxeddpF55U8L3S5zZl7a/W7s60+2cao6DsBuKrhMVe+052/GGvjrgZOJRaQe1v0MvbCGRpipql+Xdky3aroi0gx4BauKndcIpKpeuTnCMAzDXeW5kGYXsONKWF3stcZC86OAp1Q1192L++UZ8ObfwFvARVjNDX5+jdAwjL+jSux+uxtokG++PlC4DaUT8JVd4MYAl4tIjqp+e7rx1VLVuVjNETtUdRhWddowDMOvVGLvhWVAMxFpIiI1gOuBGfkTqGoTVW2sqo2Br4H7Sitwwf2a7jERcQB/isgDwB6sJ0gYhmH4FSmjV4K7VDXHLu9mA07gY1VdJyL32OvHVGS/7ha6DwPBwFBgOFYt95aKHNAwDMOTKrH3Aqo6C5hVaFmxha2q3urOPt0dT/fkSDFZwG0iEgAMwroV2DAMw2+U1f/W18oa8CZMRJ4RkdEicqlYHgC2ANd5J0TDMAz3VeYdaZ5QVk33c+Ag1kMo7wSeAGoAV6lqomdDMwzDKD8/H8O8zEL3DFVtCyAi44EUoKGqZno8MsMwjArw9wFvyip0T5x8YXf+3WYKXMMw/Jm/t+mWVei2E5GTo6kIUMueF0BVtejgm4ZhGD5Umb0XPKGs8XR9+6gFwzCMcqrqNV3DMIwqxd/bdMu80CciThH5nzeCMQzDOF3+3mWszEJXVXOBIyLiH8PCG4ZhlMJRjskX3B57AVgjIj8BecPDq+pQj0RlGIZRQQGO6tGm+709GYZh+LWqfnMEAKo6QURqYd0Ysak8Bwh01K5QYIb3VccnLABMvqlCg0H5tbfn3OrrEPxWlb+QBiAiVwKJwI/2fHsRmVHqRoZhGD4gom5PvuBuTXwY1kPa0gHscReaeCQiwzCM0+DvvRfcbdPNUdVDhZ4B5N+t1YZh/C1VizZdYK2I3AA47YdUDgV+81xYhmEYFePvvRfc/VJ4EGgNZAOTgAysp0kYhmH4lWrRvKCqR4DnRORVa9aMNGYYhn/y9wFj3Cp0ReRc4GMg1J4/BNyuqn94MDbDMIxyqy4D3nyE9WjhhQAicgHwCXC2pwIzDMOoCH/vp+tuoZt5ssAFUNVfRcQ0MRiG4XeqS6G7VETGYl1EU6wnAc8TkY4AqrrCQ/EZhmGUS6Cf9xlzt9Btb///70LLu2EVwr0qKyDDMIzTUS3adFX1Ik8HYhiGURmqS/MCItIPq69u0MllqvqiJ4IyDMOoqOrSZWwMEAxcBIwHrgGWejAuwzCMCvH3mq67Tc7dVPUW4KCqvgB0BRp4LizDMIyKCXSo25MvuNu8cNT+/4iI1AVSMaOMGYbhh/y9putuoTtTRCKA14AVWD0WPvRUUIWpKiNGjGP+/D8ICqrJyJEP0br1mUXSTZw4kwkTZrBz5z4WL55IVJT1WLfff1/Dffe9RP368QD07t2VBx4Y7K3wC1j+20bGvjEdl8tFnwGdue7Wgh0/VJWxb0xn2aIN1AyqwaP/HsSZLeoDcGv/EdQKronT4cAR4OCdzx4G4JVnPmfPjmQAsrKOEhJSi9FfPurVfOV3cO1atn81GXW5iL/wAur17VtgfVpiIru+nQ4iiNNJ40HXEdasGQArnn4GR1BNRByI08nZzz/niyyU25jX7qbvxR1ITs2gU+8nfR2O27atWM8vH05DXS7a9O5K52t6F1i/Yd4ylk6bC0BgUA0uuXcQcU3qkXP8BJOffZvcEzm4cl0069ae82+43BdZKKIyC10RuQx4G6upeLyqjiy0/kbgKXs2C7hXVVeVtk93ey8Mt19OFZGZQJCqHipP8KdjwYI/2L59L3PmjGXVqk0MG/YBU6a8USRdx44t6dnzXG655dki6zp1asXYsYV7vHlXbq6L9//zDSNGDyEmPpyH//k2Xbq3ouEZCXlplv+2kT07kxk/7Wk2rd3J6JFTGfXpQ3nrR465l/CIgk/jeOaVm/Nef/jWDGqHBOEr6nKx7csvafXII9SIjGTNiJeJbNeO4Lp189KEt2hB5L/bISIc3r2bzWPH0mH48Lz1rR97jMDQUF+EX2GfT5nPmAmzGf/Wfb4OxW2uXBdzx07hmhfuJzQ6gi8ef50zz2tDdMM6eWnC4qMZ9PJQgkKC2fbHen567ytufP0xnIEBXDv8QWrUqkluTi5fPT2KJue0pG5z3/8ArqxCV0ScwHtAb2A3sExEZqjq+nzJtgE9VPWgiPQFxgGdS43PzYMHicijIjIN+BK4XUS89pc9d+4SrrqqFyJC+/YtyMg4TFJSWpF0rVo1zavN+qPN63ZSt0E0depHExgYQPfe7Vk8f12BNEvmr+Pifp0QEVq0bcThzGOkpWS4tX9VZeH/VtGjTwdPhO+WrG3bCIqNIyg2FkdAADHnnsvBxIJf/M6gIE6OzezKzkbw89+Dbli0dCNp6Vm+DqNc9v+5g4iEWCISYnAGBtD8wo5sWbqmQJp6Lc8gKCQYgDrNG5OVmg6AiFCjVk0AXLm5uHJz/eY8OkXdnspwHrBFVbeq6nHgK2BA/gSq+puqHrRnlwD1y9qpu80LnwGZwLv2/GDgc+BaN7c/LQcOpJKQEJM3n5AQzYEDqcTFRbm9j8TETfTv/yBxcVE89dTtNGvWyBOhlio1+RAx8RF58zHxEWxau6NAmpTkQ8TmTxMXTkrSIaJiwhCB5x8Yhwj0vborff/RpcC2a1duJSI6lHoNYz2ZjVIdT0+nZtSp81IjMoLMbduKpEtdsZKd30zjREYmLYc+WGDdhlGjACG+R3fiu3f3cMR/X1mp6YTGROTNh0ZHsG/zjhLTr/lpMY07tsybd+W6mPjYa6TvS6b95RdSp3ljD0brvvLckCYiQ4Ah+RaNU9Vx9ut6wK5863ZTei32DuCHso7pbqHbXFXb5Zv/RURKbbeoTFrMF1Khp1iUqnXrpvz880fUrl2L+fOXc//9I5gzZ1zZG1Yyt/JRTKKTaV4f/wDRseGkp2Xy3APjqN84lrYdm+almz8nkZ6Xtq/MkMuvuPiLqQFFd+xAdMcOZGzezK7p02n1qNUG3ebpp6gREcGJjAzWvzWKWgkJhJ11lsfD/jsqtp5Xwt/VztWbWfu/JVz/ysN5yxxOB7eMeopjWUeY8cp4UnbsJaZR3WK396aAcpS6dgFbUmFQ3JtRwtsmF2EVuheUdUx3w1spInnVKhHpDCwqKbGIDBGR5SKyfNy4yW4eoqAvvvieAQOGMmDAUOLioti/PyVv3f795avlhoQEU7t2LQB69OhETk4uaWlea5LOExMXTsqB9Lz5lAPpRMWEFUoTQXL+NEmHiI610kTHWhcGI6JC6dqzDZvXnfoSzs3J5bdf1tC9d3uPxe+OGpGRZKedavo5fjCdGhERJaYPO+ssjiUlcyLTGj/pZNrAsDCiOrQna9t2D0b79xYaHUFmSnrefGZqOiFRYUXSJW/fw5z3JjHg2buoFVb06d5BIcHUb9uMbSs2eDJct1Vi88JuCnaNrQ/sLZxIRM7Gun9hgKqmlrVTdwvdzsBvIrJdRLYDi4EeIrJGRFYXTqyq41S1k6p2GjJkkJuHKOjGG/sxffo7TJ/+Dpdc0oVvv/0ZVSUxcSOhocHlKnSTkw+idg1s9erNuFwuIiOLfrg87axWDdi7M4X9e1I5cSKHBT8l0qV76wJpOndvxdzvl6OqbFyzg9ohQUTFhHHsaDZHDh8D4NjRbFYu2UyjpqcuwK1c+if1G8UVaL7whZDGjTmWlMSx5BRcOTmkLFtGZLt2BdIcTUrKOx9ZO3bgys0lICSE3Oxsco9ZeczNziZ9/Xpq1fN9zam6SmjWkPR9yRw6kEruiRw2LVxB0/PaFkiTkZzGjFc+ou/DNxNVLy5v+ZFDmRzLOgLAiezj7Fy1iSg/uZ5SiU+OWAY0E5EmIlIDuB4o8BR0EWkITANuVtXN7sTnbvPCZW6m84gePToxf/5yevceQq1aNXn55VNX8++6axgvvfQg8fHRfPbZDMaPn0ZKykH69x9Kjx7nMGLEUGbPXsSkSbNwOp0EBdXkzTefLFfzRGVxBji598mreX7oh7hylUv7n0ujpgl8P9V63Fy/gd049/yWLFu0kTuuHknNoEAe+Zf1pXUwNYuXnvwUgNwcFz0v60Cnbi3y9r1gTiI9+rT3dpaKEKeTJjcMZsOoUai6iDv/fILr1WX/vPkAJPTsQdofK0hevBhxOnHUqMFZQ+5CRDiRkcGm9z8AQHNziel8HpFt2vgyO26b8O6DXNi1JTGRoWz5fTTD3/yaCZPn+TqsUjmcTnoNuYapw97H5XLR5uIuxDSsw6offgWgXd8LWPzVjxzNPMzcsVOsbRwObnrzCQ4fzOCHURNRl6KqND+/PU3P9Y9zVVm9F1Q1R0QeAGZjdRn7WFXXicg99voxwL+AaOB9u0zJUdVOpe1XtLiGxsKJRJoCu1U1W0R6Yg1e/pmqppcd+mb/HvKngv7K2OTrECrd8ETv1/69YfJNY3wdQqV7e86tvg7BI4a06HPaReb3u35wu8zp16Cv12tf7jYvTAVyReRMrKdINMHqOmYYhuFX/P02YHcLXZeq5gD/AEap6iNAnTK2MQzD8DpHOSZfcLdN94SIDAZuAa60lwV6JiTDMIyK8/exF9wt7G/DGllshKpuE5EmwETPhWUYhlExTnF/8gV3a7oJwNOqegRAVbcBI0vfxDAMw/uqxeN6gFuBMSKSCiy0p1/z3XNsGIbhF/y9ecHdUcZuAbDH0r0Ga+Sduu5ubxiG4S0B1aHQFZGbgAuBtkAKMBqrtmsYhuFXfHDfU7m4W1MdBfwFjAF+UdXtngrIMAzjdPh5mete7wVVjQFux3oS8AgRWSoin3s0MsMwjAoQcX/yBXebF8KAhkAjoDEQDrg8F5ZhGEbF+OqmB3e527zwa75ptKru9lxIhmEYFSfVocuYqp4NICK1VfWwZ0MyDMOoOH/vMubuM9K6ish6YIM9305E3vdoZIZhGBUg5Zh8wd3mj1FAHyAVwH7EsHl4lWEYfqcSBzH3CLdvblDVXYUG/s6t/HAMwzBOj5+3Lrhd6O4SkW6A2o+tGIrd1GAYhuFPqsvNEfcAb2M9kng3MAe4350Ns05Uz44Ofa5PKTtRFXP8khBfh+AR1fEpCw9d+qmvQ/CIITv7nPY+qkWXMVVNAW70cCyGYRinzd97L5Ra6IrIv0pZrao6vJLjMQzDOC1+XuaWWdMtrk9ubeAOrCdgmkLXMAy/UqVvjlDVN06+FpFQ4CGsp0h8BbxR0naGYRi+UtVruohIFPAoVpvuBKCjGbzcMAx/VaV7L4jIa1hPAB4HtFXVLK9EZRiGUUG+evaZu8rqXfEY1hMingf2ikiGPWWKSIbnwzMMwygff78NuKw2XX/v8mYYhlFAlW5eMAzDqGr8vMw1ha5hGNWLv98cUe7mAxFx2E+SMAzD8DuV2aYrIpeJyCYR2SIiTxezXkTkHXv9ahHpWNY+3R1P90sRCROR2sB6YJOIPOHOtoZhGN7kEHV7Ko2IOIH3gL5AK2CwiLQqlKwv0MyehgAflBmfm/lopaoZwFXALKznpd3s5raGYRheU4kPpjwP2KKqW1X1ONZNYQMKpRkAfKaWJUCEiNQpbafuFrqBIhKIVehOV9UTgH/fa2cYxt9SJTYv1AN25ZvfbS8rb5oC3C10xwLbscZdWCAijQDTT9cwDL/jKMckIkNEZHm+aUi+XRVXLheubLqTpgB3h3Z8B3gn36IdInKRO9sahmF4U3n66arqOKw7bouzG2iQb74+sLcCaQpw90JavIh8JCI/2POtgH+6s61hGIY3CQ63pzIsA5qJSBP7iTnXAzMKpZkB3GL3YugCHFLVfaXt1N1+up8CnwDP2fObgcnAR25uX26qymuv/JdFC9cRFFSDYSNuoWWrhkXS7dmdwjNPfETGocO0aNmQ4SNvJTAwgOVLN/Po0A+oVy8GgIsuac+Qe/uxf18a/3p2AqkpGTgcwtXXXMANN/fyVDZK1f2cejx/d2ecDuG/szczdsqaAuvvHNiG/j3PACDA6aBpg3DOGzyJQ1nHue2qVlzX5yxUYdP2gzz11q8cP+Efj63r0TiKYT2b4XTAV2v28f6ynQXWd6kfwfgBbdl16CgAP25J4e0l2wG4o2N9Brepi6JsTDnM47M3kp3r8nYWiti2Yj2/fDgNdblo07srna/pXWD9hnnLWDptLgCBQTW45N5BxDWpR87xE0x+9m1yT+TgynXRrFt7zr/hcl9kodzGvHY3fS/uQHJqBp16P+nrcNwmUjk30qpqjog8AMwGnMDHqrpORO6x14/B6lhwObAFOII1CmOp3C10Y1T1vyLyTL5gPPoXvmjhOnbtTOLbWS+wdvU2Xhk+ic8mPVUk3TtvfcONN/eiz+Xn8vILX/Lt1EVce30PADp0PJO33y/4VCFngJNHnhhIy1YNOXz4GDdd9wpdurXkjKalXnCsdA6HMOy+LvzzudnsTznCtFFXMnfJTrbsOpSXZvzUtYyfuhaAXuc14LarW3Mo6zjx0cHc0r8Vl93zDdnHc3nnmZ5c0aMJ0/63xat5KI5D4KVeZ3Hj1ET2ZWbz3Y2d+OmvFP5MO1Ig3bI96dz2bcEvmfiQGtzWoT4XT1hKdo6L9/u15srmcXy9fr83s1CEK9fF3LFTuOaF+wmNjuCLx1/nzPPaEN3w1GcmLD6aQS8PJSgkmG1/rOen977ixtcfwxkYwLXDH6RGrZrk5uTy1dOjaHJOS+o2b+LDHLnn8ynzGTNhNuPfus/XoZRT5d0doaqzsArW/MvG5HutuPnospPc/Uo4LCLR2A3EJ6vR5TlQec3/ZRX9+ndBRGjb7gyyMo+QnFzwkKrKst83cfGlVn/kKwZ0Yd7Pq0rdb2xseF6NuXbtIJqckUDSgXSP5KE07c6KYcfeTHbtz+JEjovvF2zlkq5Fa/InXdGzCTPnbc2bD3A6CKrhxOkQgmoGkJR6pMRtval9Qhjb04+y89AxTriU7zYe4NKmMW5vH+AQggIcOEWoFejgwOFsD0brnv1/7iAiIZaIhBicgQE0v7AjW5YW/MKo1/IMgkKCAajTvDFZqekAiAg1atUEwJWbiys3F/H7G1Uti5ZuJC296g0sKOX45wvu1nQfxWq7aCoii4BY4BqPRQUkHUgnPiEybz4uPpLkA+nExobnLUtPP0xoaDABAU47TQTJSel569es2sb1/3iJ2LhwHn58IE3PrFvgGHv3pLJxwy7anN3Yk1kpVnx0MPtSTj2YY3/KEdo1jy02bVBNJ93Pqc8L7y8B4EDqEcZPW8uCCdeRfTyXhSv28OvKUtvuvSYhpCZ7M4/lze/LyqZ9naI3MHasE86PN5/LgaxsRizYwubUIxzIOs645btYcmdXjuW4WLAjjYU7fD90c1ZqOqExEXnzodER7Nu8o8T0a35aTOOOLfPmXbkuJj72Gun7kml/+YXUad7Yg9Ea/j76gls1XVVdAfQAugF3A61VdXVJ6fN3w/h4/MwKBabFdLooclWylEQtWjVg5k8v8dW05xl0w0U8NnRMgWRHjhzjiUfG8vhT1xISUqtCMZ4OKeYSqxaXH6BX54asWH+AQ1nHAQgLqcElXRpy0W1T6HbTVwQHBTDgojM8Gq+7iu0/Uyhba5My6Tp+MZd9voxPE3fzYf+2AITXDKB30xjO/2gJ5477jeBAJ1e3jPd80GUo9qyUcIl85+rNrP3fErr/81QfeofTwS2jnmLIRy+yf/MOUnb4xxdkdSXicHvyhVKPKiL/ODkB/YHmwFnAlfayYqnqOFXtpKqdbr/zCreD+e+keQweOILBA0cQGxfOgf2najlJBw4SExdRIH1EZAiZmUfIycm105yqCYeE1CI4OAiAC7q3IScnl4MHrZ9KJ07k8sTD4+jb7zx69e7gdnyVaX/KYerE1M6bT4gJJimt+CaCK7o34bv52/Lmz29fl937M0nLyCYnV5m9aAcdW8Z5PGZ37MvKpm5oUN58nZCaJGUVbCLIOp7LEfui3y/b0ghwCJFBgVzQMJJdGUdJO3qCHJfy45/JnFMnHF8LjY4gMyU9bz4zNZ2QqKK19+Tte5jz3iQGPHsXtcJqF1kfFBJM/bbN2LZigyfD/durxN4LHlHWUa8sZXK/NHXTdYN7Mmnqc0ya+hw9e7Xj+xlLUFXWrNpKSEitAk0LYNUWO53XnLlzVgAwc/oSevRqB0BKyqG8muPaNdtxuZSIiNqoKsP/9TlNzkjgpn9eUtlZcNvqzSk0qhtG/fgQAgMc9Ot+BnOX7CqSLiQ4kPPaJvC/xad6AOxNzqJ9i1iCalrNKt3a1y1wAc6XVu3PpElELRqEBRHoEK5sEc9PW1MKpIkNrpH3ul1CKA4RDh47wZ7MbDomhBMUYH0sz28YyZa04p6N6l0JzRqSvi+ZQwdSyT2Rw6aFK2h6XtsCaTKS05jxykf0ffhmouqd+gI8ciiTY1nWl+mJ7OPsXLWJqPq+r71XZ1W6TVdVy+z+4CkXdG/DooVrGdD3XwTVqsGw4bfkrRt672j+74WbiI2LYOgjV/HsEx/x/rvf0bxlA676RzcA5s5ZydeTF+B0OqgZFMgrr92BiLByxRa+/+53zmxWj8EDRwBw/0MDuKB7G6/mL9elvPDBEj556VKcDmHKnD/5c2c6gy9vDsCkWZsAuLRbI35dsYej2Tl5267alMKPv25n+jv9yc1V1m9NZfIPm7waf0lyVfm/Xzbz+cB2OEWYvHYfm1OPcNPZVnv6xNV7ufysWG4+ux45qhzLyeWB79cBkLg/g1l/JjHrpk7kupR1SVl8ucb3P8UdTie9hlzD1GHv43K5aHNxF2Ia1mHVD78C0K7vBSz+6keOZh5m7tgp1jYOBze9+QSHD2bww6iJqEtRVZqf356m53r3s1ZRE959kAu7tiQmMpQtv49m+JtfM2HyPF+H5Qb/fvaClNSOWCShSD+gNZD321FVXyxru6wTP1fLMRraDyj5QkpVdfySxr4OwSOev/y4r0OodA9d+qmvQ/CIozsnnXb183DOfLfLnNoBPbxe3XWr94KIjAGCgYuA8Vg9F5Z6MC7DMIwKqga9F4BuqnoLcFBVXwC6UvB+Y8MwDL9Qpdt08zlq/39EROoCaYD/31JjGMbfjuD0dQilcrfQnSkiEcB/gD/sZeM9EpFhGMZpKK4PvD8ptdAVkXOBXao63J4PAdYAG4G3PB+eYRhGefl3oVtWm+5Y4DiAiHQHRtrLDlHyGJSGYRg+4+83R5TVvOBU1TT79SBgnKpOBaaKSKJHIzMMw6iQql3TdYrIyYL5YuDnfOvcbQ82DMPwGn8fe6GsgnMSMF9EUrB6MCwEEJEz8fDQjoZhGBXhq2YDd5V1G/AIEZkL1AHm6Knb1xzAg54OzjAMo/z8u3mhzCYC+1nuhZdt9kw4hmEYp8ffB4k37bKGYVQrVbqfrmEYRtVThdt0DcMwqpoqfSHNMAyjqjHNC4ZhGF5larqGYRhe4++9F9x+ckRVICJDVLXajQlRHfNVHfME1TNf1TFPvuTf9fDyG+LrADykOuarOuYJqme+qmOefKa6FbqGYRh+zRS6hmEYXlTdCt3q2u5UHfNVHfME1TNf1TFPPlOtLqQZhmH4u+pW0zUMw/BrlV7oikhjEVlbaNkwEXm8so9VmdyNUUSeEZEtIrJJRPpU4vGjRSTRnvaLyJ58830KpX1YRN6vrGOfLhHJteNcKyLf2Q8xLc/280Skk/16Vnm3d2P/KiJv5Jt/XESGVeYxqhoReU5E1onIavvcdS4l7acico0346vOTE23HESkFXA90Bq4DHhfRCrlec+qmqqq7VW1PTAGeMt+/YF9zPyuxxpg3l8ctWNvA6QB91d0R6p6uaqmV1pklmzgHyISU8n79al8T3Up73ZdgSuAjqp6NnAJsKsyYzNK5vVC167VvCoiS0Vks4hcaC+/VUSmiciPIvKniPwn3zYfiMhy+5v5hXzLt4vIyyKy2F7fUURmi8hfInJPvnRPiMgy+1s9//bP2TXW/wHN3Qh/APCVqmar6jZgC3BeJbwtpfkauEJEaoL1SwKoC/zq4eNW1GKgHoCInCciv4nISvv/5vbyWiLylX0+JgO1Tm5sn9MY+/Wjdu15rYg8fBox5WBdDHqk8AoRiRWRqfbnY5mInG8vXyMiEWJJFZFb7OWfi8glItLa/gwn2vloZv/K2ygiE+xlX4tIsL3dv+z9rxWRcSLWAAH238Mo+/1ZKyLn2ctri8jH9jYrRWSAvfxWEZkiIt8Bcyr4ftQBUlQ1G0BVU1R1b0kxFnq/zhGR+SLyh/23VsdePlRE1tv5/qqCcf09qGqlTkBjYG2hZcOAx+3X84A37NeXA/+zX98KbAXCgSBgB9DAXhdl/++0tz/bnt8O3Gu/fgtYDYQCsUCSvfxSrD84wfqSmQl0B87Bepx8MBCGVYCejPEe4J5i8jYauCnf/EfANR54D/PeL3v+e2CA/fpp4LXKPuZpxpuV7/xMAS6z58OAAPv1JcBU+/WjwMf267OxCsVO+c5pTL7zUxsIAdYBHSoanx3Ldvvz9TgwzF73JXCB/bohsMF+PQboB7QBlgEf2sv/tON5F7jRXlYD64ujMaDA+fbyj/N9pqLyxfM5cGW+v4eT++6O/bcDvHzyswZEAJvt9+JWYHf+/VXg/QgBEu19vg/0KCPGT4FrgEDgNyDWXj4o33ncC9Q8Ga+vP5P+PHli7IWSukPkXz7N/v8PrA/qSXNV9RCAiKwHGmH97LlORIZgjRVRB2iFVcACzLD/XwOEqGomkCkix+y2wUvtaaWdLgRohlU4f6OqR+zjndwPqjqmhDwUd1O3N7p/TMJqUphu/3+7F45ZHrXEejp0Y6xz+pO9PByYICLNsN6nQHt5d+AdAFVdLSKrKeoCrPNzGEBEpgEXcuo8louqZojIZ8BQrOf9nXQJ0CpfpS5MREKxngfYHevL/wNgiIjUA9JUNUtEFgPPiUh9YJqq/mnvY5eqLrL3NdE+3uvARSLyJNaXfBTWl8h3drpJdowLRCQs3+e2v5y6zhCE9aUA8JOeekp3Rd6LLBE5B+v9vAiYLCJPY/3dlBQjWL8G2wA/2Xl1AvvsdauBL0TkW+Dbisb2d+CJ5oVUILLQsiggJd98tv1/LgUH3cnO9zoXCBCRJlg1k4vVan/6HusDWHgbV6HtXfa+BXhF7fZSVT1TVT+y05S3wNwNNMg3Xx/rG97TvgUuFpGOQC1VXeGFY5bHUbXanxth1fpOtukOB35Rq633Sgqet7Lee0+MWjIKuAOrxniSA+ia7/NRz/7iXoBVKF2IVRtNxqrtLQRQ1S+B/lgF+GwR6WXvr3C+VESCsGqU16hqW+BDSn8vFCv/A/PF1VBVN9jrD1co9/kPoJqrqvNU9d/AA8CNZcSIHdO6fDG1VdVL7XX9gPewfqH8IRVsb/47qPRCV1WzgH0icjGAiERhXXSqaBtkGNaH7JCIxAN9y7n9bOB2EQmx46knInFYf1RX2+2LoViFQllmANeLSE37y6AZsLSc8ZSb/Z7Ow/q56k8X0Aqwf6UMBR4XkUCsmu4ee/Wt+ZIuwPojR0TaYDUxFLYAuEpEgkWkNnA1doF3GvGlAf/FKnhPmoNV6GDH095OuwurmaOZqm7F+vw+zqknYp8BbFXVd7A+Fyfz0FCsC1UAg+3tThZeKfbnsHBPgEH2Pi8ADtnv42zgwXxtvx1OJ+/5iUhz+9fHSe2BTWXEiJ0m9mT+RCTQbtt2YDUF/gI8idUcElJZ8VY3nvo2ugV4T05103lBVf+qyI5UdZWIrMT6qbMVWFTGJoW3nyMiLYHF9uc3C6utbIVYF3ESsX5C5v1Bi30RrnAzg6quE5H/Auux2iHvV9XciuSrAiZhNcsU7sngV1R1pYisworzP1jNC48CP+dL9gHwid2skEgxX1z2+fk037rxqlqhpoVC3iBfIYv1JfGeHUsAVmF/8iLs71g/ocH6fLzCqcrDIOAmETkB7AdexKogbAD+KSJjsdp/P1DVIyLyIVYT2HasNuL8DorIb/b2J5uOhmPVzFfbBe92rB4HlSEEeNduxsjBup4xBEgvJUZU9bhYXcfeEZFwrPdrFFbb8ER7mWD1vEmvpFirHXNHmmFUErF6lsy0m1Pc3WYe1sW25Z6Ky/Avpp+uYRiGF5marmEYhheZmq5hGIYXmULXMAzDi0yhaxiG4UWm0DUMw/AiU+gahmF4kSl0DcMwvOj/AdTEse6+CWhIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(advertising.corr() , cmap = 'YlGnBu'  , annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9a9a160",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
    "x = advertising[feature_cols]\n",
    "y = advertising['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81cf838e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression Line for test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c20b624",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into Train and Test (70:30)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train_lm, X_test_lm, Y_train_lm, Y_test_lm = train_test_split(x,y,train_size =0.7, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "abca5914",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"X_train_lm = X_train_lm.values.reshape(-1,1)\n",
    "X_test_lm = X_test_lm.values.reshape(-1,1)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "349e2048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the model using scikit-learn\n",
    "\n",
    "from sklearn.linear_model import LinearRegression \n",
    "lm = LinearRegression() \n",
    "\n",
    "lm.fit(X_train_lm,Y_train_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "21255dc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9372157346906125\n"
     ]
    }
   ],
   "source": [
    "print (lm.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3977bcf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TV', 0.04695204776848464),\n",
       " ('Radio', 0.17658643526817366),\n",
       " ('Newspaper', 0.001851153318892224)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(feature_cols, lm.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "89073e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_pred = lm.predict(X_train_lm)\n",
    "Y_test_pred = lm.predict(X_test_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5f299efe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8850053786777521\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(r2_score(Y_train_lm,Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b083d405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9224605706201434\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(Y_test_lm,Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "490846f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Error analysis\n",
    "y_train_pred = lm.predict(X_train_lm)\n",
    "res= (Y_train_lm-y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc832db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plotting the histogram of the errors\n",
    "\n",
    "sns.distplot(res,bins =15)\n",
    "plt.title('Error terms')\n",
    "plt.xlabel ('Residual error')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c889f9f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting\n",
    "y_test_pred = lm.predict(X_test_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4f41b328",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test = (Y_test_lm - y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "762ec1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plotting the Histogram of the errors \n",
    "\n",
    "sns.distplot(res_test,bins =15)\n",
    "plt.title('Test data Error terms')\n",
    "plt.xlabel ('Residual errors')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbcdd239",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}