07 Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 7.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Implement and demonstrate  Single, Multi variable and Polynomial Regression for a given set of training data stored in a .CSV file and evaluate the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 576
    },
    "id": "o3UNBrZVmXk_",
    "outputId": "f042c430-8df6-45cd-e3ba-636b71fe5823"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual values: [95 86 17 69 42 75 54 27 62]\n",
      "Predicted values: [90.05323389 79.0394557  11.95553405 75.03444546 33.98309041 86.04822364\n",
      " 49.00187884 32.98183785 60.01565703]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 42.8684601892673\n",
      "Predicted Value for 5.2: [53.00688909]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\venki\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd  # For data manipulation\n",
    "import matplotlib.pyplot as plt  # For visualization\n",
    "from sklearn.linear_model import LinearRegression  # For the linear regression model\n",
    "from sklearn.model_selection import train_test_split  # For splitting data\n",
    "from sklearn.metrics import mean_squared_error  # For evaluating the model\n",
    "\n",
    "# Load data from CSV file\n",
    "df = pd.read_csv('./00 Lab_files/Student.csv')\n",
    "\n",
    "# Select features and target\n",
    "x = df.iloc[:,:-1]  # Features\n",
    "y = df.iloc[:, -1]  # Target\n",
    "\n",
    "# Split data into training and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=40)\n",
    "\n",
    "# Initialize and train the model\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = model.predict(x_test)\n",
    "print('Actual values:', y_test.values)\n",
    "print('Predicted values:', y_pred)\n",
    "\n",
    "# Plot data and predictions\n",
    "plt.scatter(x, y, color='red', label='Actual Data')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.plot(x_test, y_pred, color='blue', linewidth=2, label='Predicted Line')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print Mean Squared Error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error:', mse)\n",
    "\n",
    "# Predict value for a new data point\n",
    "y1 = model.predict([[5.2]])\n",
    "print('Predicted Value for 5.2:', y1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Following are Additional Things, Not Required in Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZtJT2NTymdVc"
   },
   "source": [
    " ## Evaluate R-squared Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared Score: 0.93\n"
     ]
    }
   ],
   "source": [
    "r_squared = model.score(x_test, y_test)\n",
    "print(f'R-squared Score: {r_squared:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Training vs. Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x_train, y_train, color='green', label='Training Data')\n",
    "plt.scatter(x_test, y_test, color='orange', label='Testing Data')\n",
    "plt.plot(x_test, y_pred, color='blue', linewidth=2, label='Prediction Line')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Training and Testing Data with Regression Line')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lWz4FNskebldEx4hS7QvFi3AMAABQCF7eXoqfHn924sKAbE3HT4vnesflHP96AAAAhRTTJ0b9F/aXPcz9Dmv2Bnb1X9if6xxXANwEBAAA4DLE9IlRs17NuENeBUU4BgAAuExe3l6K6hLl6TZQAtjFAQAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwOLj6QZweZy5TqWtSVNWepYC6gUoomOEvLzZxwEAACgOhONyJGVRipJGJ8lxwOEaszewK356vGL6xHiwMwBlDTvSAFA0FeqTcvLkybLZbG6P6OjoApdZsGCBoqOjVbVqVbVs2VLLli0rpW4vT8qiFM3vN98tGEuS46BD8/vNV8qiFA91BqCsSVmUoulR0zX7htladOcizb5htqZHTedzAkCZ4Mx1au+Xe/Xde99p75d75cx1erolNxXuyPHVV1+tzz//3DXt45P/S1y3bp0GDBighIQE/fnPf9a7776r3r17a+vWrWrRokVptFsozlynkkYnSSaPmUaSTUoak6RmvZpxZAio5M7tSF/4eXFuR7r/wv78pQmAx5SHv4JXuCTl4+Oj0NBQ16NOnTr51k6fPl3x8fF69NFHFRMTo6lTp6pt27b673//W4odX1ramrSLjhi7MZJjv0Npa9JKrykAZc4ld6R1dke6rB2lAVA5lJe/gle4cLxz507Vr19fjRo10sCBA5WWln9gXL9+vbp16+Y21r17d61fv77A58jOzpbD4XB7lKSs9KxirQNQMbEjDaCsKk877xUqHHfo0EGzZs1SUlKSZsyYoT179qhjx47Kyso7NGZkZKhu3bpuY3Xr1lVGRkaBz5OQkKDAwEDXIzw8vNheQ14C6gUUax2AiokdaQBlVXnaea9Q4bhHjx66/fbb1apVK3Xv3l3Lli3TsWPHNH/+/GJ9nokTJyozM9P12L9/f7Gu/0IRHSNkb2CXbPkU2CR7uF0RHSNKtA8AZRs70gDKqvK0816hwvGFgoKCdNVVV2nXrl15zg8NDdXhw4fdxg4fPqzQ0NAC1+vn5ye73e72KEle3l6Knx5/duLCgGxNx0+L58t4QCXHjjSAsqo87bxX6DR1/Phx7d69W/Xq1ctzfmxsrFauXOk2tmLFCsXGxpZGe5clpk+M+i/sL3uYexC3N7Dz7XMAktiRBlB2laedd5sxJq9To8ulRx55RLfccosiIyN16NAhTZo0ScnJyfq///s/BQcHa9CgQQoLC1NCQoKks5dy69y5s5577jn17NlT77//vp599tnLvpSbw+FQYGCgMjMzS/woMhf2B3ApeV4qKdyu+Gll51JJACof16UmJfcv5lmBuaQP9hU2r1Wo6xwfOHBAAwYM0C+//KLg4GBdf/312rBhg4KDgyVJaWlp8vL6X5CMi4vTu+++qyeeeEKPPfaYmjZtqsWLF5epaxxfyMvbS1FdojzdBoAyLKZPjJr1asaONIAy5dxfwfO8znEZ2nmvUEeOPaU0jxwDAACUZ576K3ilPHIMAACAsq2s/xWcv7EBAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAlgoVjhMSEvSHP/xBAQEBCgkJUe/evZWamlrgMrNmzZLNZnN7VK1atZQ6BgAAQFlSocLx6tWrNWLECG3YsEErVqxQTk6ObrrpJp04caLA5ex2u9LT012Pffv2lVLHAAAAKEt8PN1AcUpKSnKbnjVrlkJCQrRlyxZ16tQp3+VsNptCQ0NLuj0AAACUcRXqyPGFMjMzJUm1atUqsO748eOKjIxUeHi4evXqpe+//77A+uzsbDkcDrcHAAAAyr8KG46dTqfGjBmj6667Ti1atMi3rlmzZnrrrbf00Ucfac6cOXI6nYqLi9OBAwfyXSYhIUGBgYGuR3h4eEm8BAAAAJQymzHGeLqJkvDAAw/o008/1dq1a9WgQYNCL5eTk6OYmBgNGDBAU6dOzbMmOztb2dnZrmmHw6Hw8HBlZmbKbrdfce8AAAAoXg6HQ4GBgZfMaxXqnONzRo4cqaVLl+qrr766rGAsSVWqVFGbNm20a9eufGv8/Pzk5+d3pW0CAACgjKlQp1UYYzRy5Eh9+OGHWrVqlRo2bHjZ68jNzdV3332nevXqlUCHAAAAKMsq1JHjESNG6N1339VHH32kgIAAZWRkSJICAwNVrVo1SdKgQYMUFhamhIQESdKUKVP0xz/+UU2aNNGxY8f0z3/+U/v27dOwYcM89joAAADgGRUqHM+YMUOS1KVLF7fxxMREDRkyRJKUlpYmL6//HTD/7bffNHz4cGVkZKhmzZpq166d1q1bp+bNm5dW2wAAACgjKuwX8kpTYU/wBgAAgGdU6i/kAQBwJZy5TqWtSVNWepYC6gUoomOEvLwr1Nd0AOSDcAwAwHlSFqUoaXSSHAf+d4MnewO74qfHK6ZPjAc7A1Aa2A0GAMCSsihF8/vNdwvGkuQ46ND8fvOVsijFQ50BKC2EYwAAdPZUiqTRSVJe38SxxpLGJMmZ6yzVvgCULsIxAACS0takXXTE2I2RHPsdSluTVnpNASh1hGMAACRlpWcVax2A8olwDACApIB6AcVaB6B8IhwDACApomOE7A3ski2fAptkD7cromNEqfYFoHQRjgEAkOTl7aX46fFnJy4MyNZ0/LR4rncMVHD8hgMAYInpE6P+C/vLHuZ+9yx7A7v6L+zPdY6BSoCbgAAAcJ6YPjFq1qsZd8gDKinCMQAAF/Dy9lJUlyhPtwHAA9gNBgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACyEYwAAAMBSIcPxyy+/rKioKFWtWlUdOnTQxo0bC6xfsGCBoqOjVbVqVbVs2VLLli0rpU4BAABQllS4cDxv3jw9/PDDmjRpkrZu3arWrVure/fuOnLkSJ7169at04ABA3Tvvffq22+/Ve/evdW7d2/t2LGjlDsHAACAp9mMMcbTTRSnDh066A9/+IP++9//SpKcTqfCw8P10EMPacKECRfV33HHHTpx4oSWLl3qGvvjH/+oa665RjNnzizUczocDgUGBuro0aOy2+0Xzffy8pKPj49r+vTp0/muy2azqUqVKkWqzcnJUX7/nCVVK0m+vr5Fqj1z5oycTmex1FapUkU2m61Ea3Nzc5Wbm1sstT4+PvLy8ioztU6nU2fOnMm31tvbW97e3mWm1hijnJycYqk9//ezpGqlgn+X+YzIu5bPCD4j+Iy4/Fo+I/KvPXbsmIKDg5WZmZlnXjvHJ9855dDp06e1ZcsWTZw40TXm5eWlbt26af369Xkus379ej388MNuY927d9fixYvzfZ7s7GxlZ2e7ph0OhyTpX//6l6pWrXpRfdOmTXXnnXe6pl944YV8f2EiIyM1ZMgQ1/T06dN18uTJPGvr16+v4cOHu6ZffvllZWZm5lkbHBysBx980DX9+uuv6+jRo3nWBgYGasyYMa7pWbNm6dChQ3nWVq9eXY8++qhreu7cudq3b1+etVWqVNFjjz3mmp4/f7527tyZZ60kTZo0yfXzhx9+qP/7v//Lt3bixImuX4KlS5dq27Zt+dY+8sgjqlGjhiTps88+0+bNm/OtHT16tIKCgiRJK1euzPd9JEkPPPCAQkJCJElr1qzR6tWr860dNmyYwsLCJEkbNmzQ559/nm/t4MGDFRUVJUnasmWLPv3003xrBwwYoKuuukqS9N133+mjjz7Kt7Zfv366+uqrJUkpKSlauHBhvrW9evXSNddcI0natWuX3nvvvXxre/TooWuvvVaSlJaWptmzZ+db261bN1133XWSpPT0dL3xxhv51nbu3FldunSRJB09elQzZszItzY2NlY33XSTJCkzM1PTp0/Pt7Z9+/bq2bOnJOnkyZN64YUX8q1t3bq1evfuLensB3hCQkK+tc2bN9ftt9/umi6ols+Is/iM+B8+I87iM+IsPiPOutLPiK1bt+Zbe74KdVrFzz//rNzcXNWtW9dtvG7dusrIyMhzmYyMjMuql86+gQMDA12P8PDwK28eAAAAHlehTqs4dOiQwsLCtG7dOsXGxrrG//a3v2n16tX65ptvLlrG19dXs2fP1oABA1xjr7zyip5++mkdPnw4z+fJ68hxeHg4p1VcZi1/MuVPpvzJ9PJr+YwoWi2fEVdWWxZ+7/mM4DPiwlpOqyiEOnXqyNvb+6JQe/jwYYWGhua5TGho6GXVS5Kfn5/8/PwuGvf19XX7h8hPYWqKUnv+G7E81J7/i14eas//MK1otV5eXoV+r5WFWpvNVq5qpZL7veczouzUloXfZT4jzioLv/d8RpR87eX+Lhf6vVbotZYDvr6+ateunVauXOkaczqdWrlypduR5PPFxsa61UvSihUr8q0HAABAxVVsR46PHTvm+lKCJz388MMaPHiw2rdvr2uvvVbTpk3TiRMndM8990iSBg0apLCwMNeJ76NHj1bnzp31r3/9Sz179tT777+vzZs367XXXvPkywAAAIAHFOnI8fPPP6958+a5pvv376/atWsrLCyswG8Al4Y77rhDL7zwgp566ildc801Sk5OVlJSkutLd2lpaUpPT3fVx8XF6d1339Vrr72m1q1ba+HChVq8eLFatGjhqZcAAAAADynSF/IaNmyouXPnKi4uTitWrFD//v01b948zZ8/X2lpaVq+fHlJ9FpmnbvO8aVO8AYAAIBnFDavFem0ioyMDNfly5YuXar+/fvrpptuUlRUlDp06FC0jgEAAAAPK9JpFTVr1tT+/fslSUlJSerWrZuks5ctKeiyMAAAAEBZVqQjx3369NGdd96ppk2b6pdfflGPHj0kSd9++62aNGlSrA0CAAAApaVI4fjFF19UVFSU9u/fr3/84x/y9/eXdPbWjuffWhAAAAAoTyrUHfI8hS/kAQAAlG3F/oW8JUuWFPrJb7311kLXAgAAAGVFocNx7969C1Vns9n4Uh4AAADKpUKHY6fTWZJ9AAAAAB5XpEu5AQAAABVRka5WIUknTpzQ6tWrlZaWptOnT7vNGzVq1BU3BgAAAJS2IoXjb7/9VjfffLNOnjypEydOqFatWvr5559VvXp1hYSEEI4BAABQLhXptIqxY8fqlltu0W+//aZq1appw4YN2rdvn9q1a6cXXnihuHsEAAAASkWRwnFycrLGjRsnLy8veXt7Kzs7W+Hh4frHP/6hxx57rLh7BAAAAEpFkcJxlSpV5OV1dtGQkBClpaVJkgIDA7V///7i6w4AAAAoRUU657hNmzbatGmTmjZtqs6dO+upp57Szz//rHfeeUctWrQo7h4BAACAUlGkI8fPPvus6tWrJ0l65plnVLNmTT3wwAM6evSoXnvttWJtEAAAACgtNmOM8XQT5V1h79UNAAAAzyhsXuMmIAAAAIClSOccN2zYUDabLd/5P/30U5EbAgAAADylSOF4zJgxbtM5OTn69ttvlZSUpEcffbQ4+gIAAABKXZHC8ejRo/Mcf/nll7V58+YraggAAADwlGI957hHjx764IMPinOVAAAAQKkp1nC8cOFC1apVqzhXCQAAAJSaIt8E5Pwv5BljlJGRoaNHj+qVV14ptuYAAACA0lSkcNy7d2+3aS8vLwUHB6tLly6Kjo4ujr4AAACAUsdNQIoBNwEBAAAo2wqb1wp95NjhcBT6yQmIAAAAKI8KHY6DgoIKvPHH+XJzc4vcEAAAAOAphQ7HX3zxhevnvXv3asKECRoyZIhiY2MlSevXr9fs2bOVkJBQ/F0CAAAApaBI5xx37dpVw4YN04ABA9zG3333Xb322mv68ssvi6u/coFzjgEAAMq2wua1Il3neP369Wrfvv1F4+3bt9fGjRuLskoAAADA44oUjsPDw/X6669fNP7GG28oPDz8ipsCAAAAPKFI1zl+8cUX1bdvX3366afq0KGDJGnjxo3auXMnt48GAABAuVWkI8c333yzfvzxR91yyy369ddf9euvv+qWW27Rjz/+qJtvvrm4ewQAAABKBTcBKQZ8IQ8AAKBsK/abgGzfvl0tWrSQl5eXtm/fXmBtq1atCt8pAAAAUEYUOhxfc801ysjIUEhIiK655hrZbDblddDZZrNxExAAAACUS4UOx3v27FFwcLDrZwAAAKCiKXQ4joyMzPNnAAAAoKIo0tUqZs+erU8++cQ1/be//U1BQUGKi4vTvn37iq05AAAAoDQVKRw/++yzqlatmqSzd8v773//q3/84x+qU6eOxo4dW6wNAgAAAKWlSDcB2b9/v5o0aSJJWrx4sfr166f77rtP1113nbp06VKc/QEAAAClpkhHjv39/fXLL79IkpYvX64bb7xRklS1alX9/vvvxdcdAAAAUIqKdOT4xhtv1LBhw9SmTRu3u+J9//33ioqKKs7+AAAAgFJTpCPHL7/8smJjY3X06FF98MEHql27tiRpy5YtGjBgQLE2CAAAAJSWCnP76L1792rq1KlatWqVMjIyVL9+fd111116/PHH5evrm+9yXbp00erVq93G/vrXv2rmzJmFfm5uHw0AAFC2Ffvtoy+0Zs0avfrqq/rpp5+0YMEChYWF6Z133lHDhg11/fXXF3W1RfbDDz/I6XTq1VdfVZMmTbRjxw4NHz5cJ06c0AsvvFDgssOHD9eUKVNc09WrVy/pdgEAAFAGFSkcf/DBB7r77rs1cOBAbd26VdnZ2ZKkzMxMPfvss1q2bFmxNlkY8fHxio+Pd003atRIqampmjFjxiXDcfXq1RUaGlro58rOzna9ZunsnggAAADKvyKdc/z3v/9dM2fO1Ouvv64qVaq4xq+77jpt3bq12Jq7UpmZmapVq9Yl6+bOnas6deqoRYsWmjhxok6ePFlgfUJCggIDA12P8PDw4moZAAAAHlSkI8epqanq1KnTReOBgYE6duzYlfZULHbt2qX//Oc/lzxqfOeddyoyMlL169fX9u3bNX78eKWmpmrRokX5LjNx4kQ9/PDDrmmHw0FABgAAqACKFI5DQ0O1a9euiy7btnbtWjVq1Kg4+nKZMGGCnn/++QJrUlJSFB0d7Zo+ePCg4uPjdfvtt2v48OEFLnvfffe5fm7ZsqXq1aunrl27avfu3WrcuHGey/j5+cnPz+8yXgUAAADKgyKF4+HDh2v06NF66623ZLPZdOjQIa1fv17jxo3TU089VawNjhs3TkOGDCmw5vxAfujQId1www2Ki4vTa6+9dtnP16FDB0lnjzznF44BAABQMRUpHE+YMEFOp1Ndu3bVyZMn1alTJ/n5+enRRx/VsGHDirXB4OBgBQcHF6r24MGDuuGGG9SuXTslJibKy+vyT6lOTk6WJNWrV++ylwUAAED5VqQv5NlsNj3++OP69ddftWPHDm3YsEFHjx5VYGCgGjZsWNw9FsrBgwfVpUsXRURE6IUXXtDRo0eVkZGhjIwMt5ro6Ght3LhRkrR7925NnTpVW7Zs0d69e7VkyRINGjRInTp1UqtWrTzyOgAAAOA5l3XkODs7W5MnT9aKFStcR4p79+6txMRE3XbbbfL29tbYsWNLqtcCrVixQrt27dKuXbvUoEEDt3nn7nOSk5Oj1NRU19UofH199fnnn2vatGk6ceKEwsPD1bdvXz3xxBOl3j8AAAA877LukDd+/Hi9+uqr6tatm9atW6ejR4/qnnvu0YYNG/TYY4/p9ttvl7e3d0n2WyZxhzwAAICyrUTukLdgwQK9/fbbuvXWW7Vjxw61atVKZ86c0bZt22Sz2a64aQAAAMCTLuuc4wMHDqhdu3aSpBYtWsjPz09jx44lGAMAAKBCuKxwnJubK19fX9e0j4+P/P39i70pAAAAwBMu67QKY4yGDBniugHGqVOndP/996tGjRpudQXdXQ4AAAAoqy4rHA8ePNht+q677irWZgAAAABPuqxwnJiYWFJ9AAAAAB5XpJuAAAAAABUR4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwVKhwHBUVJZvN5vZ47rnnClzm1KlTGjFihGrXri1/f3/17dtXhw8fLqWOAQAAUJZUqHAsSVOmTFF6errr8dBDDxVYP3bsWH388cdasGCBVq9erUOHDqlPnz6l1C0AAADKEh9PN1DcAgICFBoaWqjazMxMvfnmm3r33Xf1pz/9SZKUmJiomJgYbdiwQX/84x/zXC47O1vZ2dmuaYfDceWNAwAAwOMq3JHj5557TrVr11abNm30z3/+U2fOnMm3dsuWLcrJyVG3bt1cY9HR0YqIiND69evzXS4hIUGBgYGuR3h4eLG+BgAAAHhGhTpyPGrUKLVt21a1atXSunXrNHHiRKWnp+vf//53nvUZGRny9fVVUFCQ23jdunWVkZGR7/NMnDhRDz/8sGva4XAQkAEAACqAMh+OJ0yYoOeff77AmpSUFEVHR7sF1latWsnX11d//etflZCQID8/v2Lryc/Pr1jXBwAAgLKhzIfjcePGaciQIQXWNGrUKM/xDh066MyZM9q7d6+aNWt20fzQ0FCdPn1ax44dczt6fPjw4UKftwwAAICKo8yH4+DgYAUHBxdp2eTkZHl5eSkkJCTP+e3atVOVKlW0cuVK9e3bV5KUmpqqtLQ0xcbGFrlnAAAAlE9lPhwX1vr16/XNN9/ohhtuUEBAgNavX6+xY8fqrrvuUs2aNSVJBw8eVNeuXfX222/r2muvVWBgoO699149/PDDqlWrlux2ux566CHFxsbme6UKAAAAVFwVJhz7+fnp/fff1+TJk5Wdna2GDRtq7Nixbuch5+TkKDU1VSdPnnSNvfjii/Ly8lLfvn2VnZ2t7t2765VXXvHESwAAAICH2YwxxtNNlHcOh0OBgYHKzMyU3W73dDsAAAC4QGHzWoW7zjEAAABQVIRjAAAAwEI4BgAAACyEYwAAAMBCOAYAAAAshGMAAADAQjgGAAAALIRjAAAAwEI4BgAAACwV5vbRAK6cM9eptDVpykrPUkC9AEV0jJCXN/vQAIDKg3AMQJKUsihFSaOT5DjgcI3ZG9gVPz1eMX1iPNgZABSMHXsUJ8IxAKUsStH8fvMl4z7uOOjQ/H7z1X9hfwIygDKJHXsUN3argErOmetU0uiki4KxJNdY0pgkOXOdpdoXAFzKuR3784Ox9L8d+5RFKR7qDOUZ4Rio5NLWpF30H4sbIzn2O5S2Jq30mgKAS2DHHiWFcAxUclnpWcVaBwClgR17lBTCMVDJBdQLKNY6ACgN7NijpBCOgUouomOE7A3ski2fAptkD7cromNEqfYFAAVhxx4lhXAMVHJe3l6Knx5/duLCgGxNx0+L57JIAMoUduxRUvjfDoBi+sSo/8L+sofZ3cbtDexcxg1AmcSOPUqKzRiT1/c8cRkcDocCAwOVmZkpu91+6QWAMooL6QMob/K8znG4XfHTuM4x3BU2rxGOiwHhGAAAz2HHHoVR2LzGHfIAAEC55uXtpaguUZ5uAxUEu1UAAACAhXAMAAAAWAjHAAAAgIVwDAAAAFgIxwAAAICFcAwAAABYCMcAAACAhXAMAAAAWAjHAAAAgIVwDAAAAFgIxwAAAICFcAwAAABYCMcAAACAhXAMAAAAWAjHAAAAgIVwDAAAAFgIxwAAAICFcAwAAABYCMcAAACAhXAMAAAAWAjHAAAAgIVwDAAAAFgqTDj+8ssvZbPZ8nxs2rQp3+W6dOlyUf39999fip0DAACgrPDxdAPFJS4uTunp6W5jTz75pFauXKn27dsXuOzw4cM1ZcoU13T16tVLpEcAAACUbRUmHPv6+io0NNQ1nZOTo48++kgPPfSQbDZbgctWr17dbVkAAABUThXmtIoLLVmyRL/88ovuueeeS9bOnTtXderUUYsWLTRx4kSdPHmywPrs7Gw5HA63BwAAAMq/CnPk+EJvvvmmunfvrgYNGhRYd+eddyoyMlL169fX9u3bNX78eKWmpmrRokX5LpOQkKCnn366uFsGAACAh9mMMcbTTRRkwoQJev755wusSUlJUXR0tGv6wIEDioyM1Pz589W3b9/Ler5Vq1apa9eu2rVrlxo3bpxnTXZ2trKzs13TDodD4eHhyszMlN1uv6znAwAAQMlzOBwKDAy8ZF4r80eOx40bpyFDhhRY06hRI7fpxMRE1a5dW7feeutlP1+HDh0kqcBw7OfnJz8/v8teNwAAAMq2Mh+Og4ODFRwcXOh6Y4wSExM1aNAgValS5bKfLzk5WZJUr169y14WAAAA5VuF+0LeqlWrtGfPHg0bNuyieQcPHlR0dLQ2btwoSdq9e7emTp2qLVu2aO/evVqyZIkGDRqkTp06qVWrVqXdOgAAADyszB85vlxvvvmm4uLi3M5BPicnJ0epqamuq1H4+vrq888/17Rp03TixAmFh4erb9++euKJJ0q7bQAAAJQBZf4LeeVBYU/wBgAAgGcUNq9VuNMqAAAAgKIiHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAACWchOOn3nmGcXFxal69eoKCgrKsyYtLU09e/ZU9erVFRISokcffVRnzpwpcL2//vqrBg4cKLvdrqCgIN177706fvx4CbwCAAAAlHXlJhyfPn1at99+ux544IE85+fm5qpnz546ffq01q1bp9mzZ2vWrFl66qmnClzvwIED9f3332vFihVaunSpvvrqK913330l8RIAAABQxtmMMcbTTVyOWbNmacyYMTp27Jjb+Keffqo///nPOnTokOrWrStJmjlzpsaPH6+jR4/K19f3onWlpKSoefPm2rRpk9q3by9JSkpK0s0336wDBw6ofv36herJ4XAoMDBQmZmZstvtV/YCAQAAUOwKm9fKzZHjS1m/fr1atmzpCsaS1L17dzkcDn3//ff5LhMUFOQKxpLUrVs3eXl56Ztvvsn3ubKzs+VwONweAAAAKP8qTDjOyMhwC8aSXNMZGRn5LhMSEuI25uPjo1q1auW7jCQlJCQoMDDQ9QgPD7/C7gEAAFAWeDQcT5gwQTabrcDHDz/84MkW8zRx4kRlZma6Hvv37/d0SwAAACgGPp588nHjxmnIkCEF1jRq1KhQ6woNDdXGjRvdxg4fPuyal98yR44ccRs7c+aMfv3113yXkSQ/Pz/5+fkVqi8AAACUHx4Nx8HBwQoODi6WdcXGxuqZZ57RkSNHXKdKrFixQna7Xc2bN893mWPHjmnLli1q166dJGnVqlVyOp3q0KFDsfQFAACA8qPcnHOclpam5ORkpaWlKTc3V8nJyUpOTnZdk/imm25S8+bNdffdd2vbtm367LPP9MQTT2jEiBGuo7wbN25UdHS0Dh48KEmKiYlRfHy8hg8fro0bN+rrr7/WyJEj9Ze//KXQV6oAAABAxeHRI8eX46mnntLs2bNd023atJEkffHFF+rSpYu8vb21dOlSPfDAA4qNjVWNGjU0ePBgTZkyxbXMyZMnlZqaqpycHNfY3LlzNXLkSHXt2lVeXl7q27evXnrppdJ7YQAAACgzyt11jssirnMMAABQtlW66xwDAAAAV4pwDAAAAFgIxwAAAICFcAwAAABYCMcAAACAhXAMAAAAWAjHAAAAgIVwDAAAAFgIxwAAAICFcAwAAABYCMcAAACAxcfTDQAAAMBznLlOpa1JU1Z6lgLqBSiiY4S8vCvv8VPCMQAAQCWVsihFSaOT5DjgcI3ZG9gVPz1eMX1iPNiZ51Te3QIAAIBKLGVRiub3m+8WjCXJcdCh+f3mK2VRioc68yzCMQAAQCXjzHUqaXSSZPKYaY0ljUmSM9dZqn2VBYRjAACASiZtTdpFR4zdGMmx36G0NWml11QZQTgGAACoZLLSs4q1riIhHAMAAFQyAfUCirWuIiEcAwAAVDIRHSNkb2CXbPkU2CR7uF0RHSNKta+ygHAMAABQyXh5eyl+evzZiQsDsjUdPy2+Ul7vuPK9YgAAACimT4z6L+wve5jdbdzewK7+C/tX2usccxMQAACASiqmT4ya9WrGHfLOQzgGAACoxLy8vRTVJcrTbZQZlXe3AAAAALgA4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAACLj6cbqAiMMZIkh8Ph4U4AAACQl3M57Vxuyw/huBhkZWVJksLDwz3cCQAAAAqSlZWlwMDAfOfbzKXiMy7J6XTq0KFDCggIkM1mK/RyDodD4eHh2r9/v+x2ewl2WD6xfQrG9ikY26dgbJ9LYxsVjO1TMLZPwTyxfYwxysrKUv369eXllf+ZxRw5LgZeXl5q0KBBkZe32+384hSA7VMwtk/B2D4FY/tcGtuoYGyfgrF9Clba26egI8bn8IU8AAAAwEI4BgAAACyEYw/y8/PTpEmT5Ofn5+lWyiS2T8HYPgVj+xSM7XNpbKOCsX0KxvYpWFnePnwhDwAAALBw5BgAAACwEI4BAAAAC+EYAAAAsBCOAQAAAAvhuIR99dVXuuWWW1S/fn3ZbDYtXrzYbb4xRk899ZTq1aunatWqqVu3btq5c6dnmvWAhIQE/eEPf1BAQIBCQkLUu3dvpaamutWcOnVKI0aMUO3ateXv76++ffvq8OHDHuq4dM2YMUOtWrVyXSQ9NjZWn376qWt+Zd42eXnuuedks9k0ZswY11hl30aTJ0+WzWZze0RHR7vmV/btI0kHDx7UXXfdpdq1a6tatWpq2bKlNm/e7JpfmT+no6KiLnr/2Gw2jRgxQhLvn9zcXD355JNq2LChqlWrpsaNG2vq1Kk6/1oHlfn9I529VfOYMWMUGRmpatWqKS4uTps2bXLNL5Pbx6BELVu2zDz++ONm0aJFRpL58MMP3eY/99xzJjAw0CxevNhs27bN3HrrraZhw4bm999/90zDpax79+4mMTHR7NixwyQnJ5ubb77ZREREmOPHj7tq7r//fhMeHm5WrlxpNm/ebP74xz+auLg4D3ZdepYsWWI++eQT8+OPP5rU1FTz2GOPmSpVqpgdO3YYYyr3trnQxo0bTVRUlGnVqpUZPXq0a7yyb6NJkyaZq6++2qSnp7seR48edc2v7Nvn119/NZGRkWbIkCHmm2++MT/99JP57LPPzK5du1w1lflz+siRI27vnRUrVhhJ5osvvjDG8P555plnTO3atc3SpUvNnj17zIIFC4y/v7+ZPn26q6Yyv3+MMaZ///6mefPmZvXq1Wbnzp1m0qRJxm63mwMHDhhjyub2IRyXogvDsdPpNKGhoeaf//yna+zYsWPGz8/PvPfeex7o0POOHDliJJnVq1cbY85ujypVqpgFCxa4alJSUowks379ek+16VE1a9Y0b7zxBtvmPFlZWaZp06ZmxYoVpnPnzq5wzDY6G45bt26d5zy2jzHjx483119/fb7z+Zx2N3r0aNO4cWPjdDp5/xhjevbsaYYOHeo21qdPHzNw4EBjDO+fkydPGm9vb7N06VK38bZt25rHH3+8zG4fTqvwoD179igjI0PdunVzjQUGBqpDhw5av369BzvznMzMTElSrVq1JElbtmxRTk6O2zaKjo5WREREpdtGubm5ev/993XixAnFxsaybc4zYsQI9ezZ021bSLx/ztm5c6fq16+vRo0aaeDAgUpLS5PE9pGkJUuWqH379rr99tsVEhKiNm3a6PXXX3fN53P6f06fPq05c+Zo6NChstlsvH8kxcXFaeXKlfrxxx8lSdu2bdPatWvVo0cPSbx/zpw5o9zcXFWtWtVtvFq1alq7dm2Z3T4+HntmKCMjQ5JUt25dt/G6deu65lUmTqdTY8aM0XXXXacWLVpIOruNfH19FRQU5FZbmbbRd999p9jYWJ06dUr+/v768MMP1bx5cyUnJ1f6bSNJ77//vrZu3ep2Dts5vH+kDh06aNasWWrWrJnS09P19NNPq2PHjtqxYwfbR9JPP/2kGTNm6OGHH9Zjjz2mTZs2adSoUfL19dXgwYP5nD7P4sWLdezYMQ0ZMkQSv1+SNGHCBDkcDkVHR8vb21u5ubl65plnNHDgQEn8Px8QEKDY2FhNnTpVMTExqlu3rt577z2tX79eTZo0KbPbh3CMMmPEiBHasWOH1q5d6+lWypRmzZopOTlZmZmZWrhwoQYPHqzVq1d7uq0yYf/+/Ro9erRWrFhx0ZEJnHXuCJYktWrVSh06dFBkZKTmz5+vatWqebCzssHpdKp9+/Z69tlnJUlt2rTRjh07NHPmTA0ePNjD3ZUtb775pnr06KH69et7upUyY/78+Zo7d67effddXX311UpOTtaYMWNUv3593j+Wd955R0OHDlVYWJi8vb3Vtm1bDRgwQFu2bPF0a/nitAoPCg0NlaSLvtl7+PBh17zKYuTIkVq6dKm++OILNWjQwDUeGhqq06dP69ixY271lWkb+fr6qkmTJmrXrp0SEhLUunVrTZ8+nW2js6cFHDlyRG3btpWPj498fHy0evVqvfTSS/Lx8VHdunUr/Ta6UFBQkK666irt2rWL95CkevXqqXnz5m5jMTExrlNP+Jw+a9++ffr88881bNgw1xjvH+nRRx/VhAkT9Je//EUtW7bU3XffrbFjxyohIUES7x9Jaty4sVavXq3jx49r//792rhxo3JyctSoUaMyu30Ixx7UsGFDhYaGauXKla4xh8Ohb775RrGxsR7srPQYYzRy5Eh9+OGHWrVqlRo2bOg2v127dqpSpYrbNkpNTVVaWlql2UYXcjqdys7OZttI6tq1q7777jslJye7Hu3bt9fAgQNdP1f2bXSh48ePa/fu3apXrx7vIUnXXXfdRZeP/PHHHxUZGSmJz+lzEhMTFRISop49e7rGeP9IJ0+elJeXe5Ty9vaW0+mUxPvnfDVq1FC9evX022+/6bPPPlOvXr3K7vbx2FcBK4msrCzz7bffmm+//dZIMv/+97/Nt99+a/bt22eMOXsJk6CgIPPRRx+Z7du3m169enn8Eial6YEHHjCBgYHmyy+/dLtc0MmTJ101999/v4mIiDCrVq0ymzdvNrGxsSY2NtaDXZeeCRMmmNWrV5s9e/aY7du3mwkTJhibzWaWL19ujKnc2yY/51+twhi20bhx48yXX35p9uzZY77++mvTrVs3U6dOHXPkyBFjDNtn48aNxsfHxzzzzDNm586dZu7cuaZ69epmzpw5rprK/jmdm5trIiIizPjx4y+aV9nfP4MHDzZhYWGuS7ktWrTI1KlTx/ztb39z1VT2909SUpL59NNPzU8//WSWL19uWrdubTp06GBOnz5tjCmb24dwXMK++OILI+mix+DBg40xZy/z8uSTT5q6desaPz8/07VrV5OamurZpktRXttGkklMTHTV/P777+bBBx80NWvWNNWrVze33XabSU9P91zTpWjo0KEmMjLS+Pr6muDgYNO1a1dXMDamcm+b/FwYjiv7NrrjjjtMvXr1jK+vrwkLCzN33HGH2zV8K/v2McaYjz/+2LRo0cL4+fmZ6Oho89prr7nNr+yf05999pmRlOdrruzvH4fDYUaPHm0iIiJM1apVTaNGjczjjz9usrOzXTWV/f0zb94806hRI+Pr62tCQ0PNiBEjzLFjx1zzy+L2sRlz3m1cAAAAgEqMc44BAAAAC+EYAAAAsBCOAQAAAAvhGAAAALAQjgEAAAAL4RgAAACwEI4BAAAAC+EYAAAAsBCOAaCMGzJkiHr37u2a7tKli8aMGVPqfXz55Zey2Ww6duxYiT6PzWbT4sWLS/Q5ACA/hGMAKIIhQ4bIZrPJZrPJ19dXTZo00ZQpU3TmzJkSf+5FixZp6tSphaotrUB7+vRp1alTR88991ye86dOnaq6desqJyenRPsAgCtFOAaAIoqPj1d6erp27typcePGafLkyfrnP/+ZZ+3p06eL7Xlr1aqlgICAYltfcfD19dVdd92lxMTEi+YZYzRr1iwNGjRIVapU8UB3AFB4hGMAKCI/Pz+FhoYqMjJSDzzwgLp166YlS5ZI+t+pEM8884zq16+vZs2aSZL279+v/v37KygoSLVq1VKvXr20d+9e1zpzc3P18MMPKygoSLVr19bf/vY3GWPcnvfC0yqys7M1fvx4hYeHy8/PT02aNNGbb76pvXv36oYbbpAk1axZUzabTUOGDJEkOZ1OJSQkqGHDhqpWrZpat26thQsXuj3PsmXLdNVVV6latWq64YYb3PrMy7333qsff/xRa9eudRtfvXq1fvrpJ917773atGmTbrzxRtWpU0eBgYHq3Lmztm7dmu868zrynZycLJvN5tbP2rVr1bFjR1WrVk3h4eEaNWqUTpw44Zr/yiuvqGnTpqpatarq1q2rfv36FfhaAFRehGMAKCbVqlVzO0K8cuVKpaamasWKFVq6dKlycnLUvXt3BQQEaM2aNfr666/l7++v+Ph413L/+te/NGvWLL311ltau3atfv31V3344YcFPu+gQYP03nvv6aWXXlJKSopeffVV+fv7Kzw8XB988IEkKTU1Venp6Zo+fbokKSEhQW+//bZmzpyp77//XmPHjtVdd92l1atXSzob4vv06aNbbrlFycnJGjZsmCZMmFBgHy1bttQf/vAHvfXWW27jiYmJiouLU3R0tLKysjR48GCtXbtWGzZsUNOmTXXzzTcrKyvr8jb2eXbv3q34+Hj17dtX27dv17x587R27VqNHDlSkrR582aNGjVKU6ZMUWpqqpKSktSpU6ciPx+ACs4AAC7b4MGDTa9evYwxxjidTrNixQrj5+dnHnnkEdf8unXrmuzsbNcy77zzjmnWrJlxOp2usezsbFOtWjXz2WefGWOMqVevnvnHP/7hmp+Tk2MaNGjgei5jjOncubMZPXq0McaY1NRUI8msWLEizz6/+OILI8n89ttvrrFTp06Z6tWrm3Xr1rnV3nvvvWbAgAHGGGMmTpxomjdv7jZ//PjxF63rQjNnzjT+/v4mKyvLGGOMw+Ew1atXN2+88Uae9bm5uSYgIMB8/PHHrjFJ5sMPP8y3/2+//dZIMnv27HH1fd9997mtd82aNcbLy8v8/vvv5oMPPjB2u904HI58+waAczhyDABFtHTpUvn7+6tq1arq0aOH7rjjDk2ePNk1v2XLlvL19XVNb9u2Tbt27VJAQID8/f3l7++vWrVq6dSpU9q9e7cyMzOVnp6uDh06uJbx8fFR+/bt8+0hOTlZ3t7e6ty5c6H73rVrl06ePKkbb7zR1Ye/v7/efvtt7d69W5KUkpLi1ockxcbGXnLdAwYMUG5urubPny9Jmjdvnry8vHTHHXdIkg4fPqzhw4eradOmCgwMlN1u1/Hjx5WWllbo/i+0bds2zZo1y+21dO/eXU6nU3v27NGNN96oyMhINWrUSHfffbfmzp2rkydPFvn5AFRsPp5uAADKqxtuuEEzZsyQr6+v6tevLx8f94/UGjVquE0fP35c7dq109y5cy9aV3BwcJF6qFat2mUvc/z4cUnSJ598orCwMLd5fn5+RerjHLvdrn79+ikxMVFDhw5VYmKi+vfvL39/f0nS4MGD9csvv2j69OmKjIyUn5+fYmNj8/3CopfX2WM45rzzri+84sXx48f117/+VaNGjbpo+YiICPn6+mrr1q368ssvtXz5cj311FOaPHmyNm3apKCgoCt6vQAqHsIxABRRjRo11KRJk0LXt23bVvPmzVNISIjsdnueNfXq1dM333zjOif2zJkz2rJli9q2bZtnfcuWLeV0OrV69Wp169btovnnjlzn5ua6xpo3by4/Pz+lpaXle8Q5JibG9eXCczZs2HDpF6mzX8zr0qWLli5dqnXr1rldwePrr7/WK6+8optvvlnS2XObf/7553zXdW6nIT09XTVr1pR09mj5+dq2bav/+7//K/DfwsfHR926dVO3bt00adIkBQUFadWqVerTp0+hXhOAyoPTKgCglAwcOFB16tRRr169tGbNGu3Zs0dffvmlRo0apQMHDkiSRo8ereeee06LFy/WDz/8oAcffLDAaxRHRUVp8ODBGjp0qBYvXuxa57nTGiIjI2Wz2bR06VIdPXpUx48fV0BAgB555BGNHTtWs2fP1u7du7V161b95z//0ezZsyVJ999/v3bu3KlHH31UqampevfddzVr1qxCvc5OnTqpSZMmGjRokKKjoxUXF+ea17RpU73zzjtKSUnRN998o4EDBxZ49LtJkyYKDw/X5MmTtXPnTn3yySf617/+5VYzfvx4rVu3TiNHjlRycrJ27typjz76yPWFvKVLl+qll15ScnKy9u3bp7fffltOp9N1BREAOB/hGABKSfXq1fXVV18pIiJCffr0UUxMjO69916dOnXKdSR53LhxuvvuuzV48GDFxsYqICBAt912W4HrnTFjhvr166cHH3xQ0dHRGj58uOsyZmFhYXr66ac1YcIE1a1b1xUYp06dqieffFIJCQmKiYlRfHy8PvnkEzVs2FDS2dMRPvjgAy1evFitW7fWzJkz9eyzzxbqddpsNg0dOlS//fabhg4d6jbvzTff1G+//aa2bdvq7rvv1qhRoxQSEpLvuqpUqaL33ntPP/zwg1q1aqXnn39ef//7391qWrVqpdWrV+vHH39Ux44d1aZNGz311FOqX7++JCkoKEiLFi3Sn/70J8XExGjmzJl67733dPXVVxfq9QCoXGzGXHABTQAAAKCS4sgxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABgIRwDAAAAFsIxAAAAYCEcAwAAABbCMQAAAGAhHAMAAAAWwjEAAABg+X+aaGu+lrxSIwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = y_test - y_pred\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_pred, residuals, color='purple')\n",
    "plt.axhline(y=0, color='gray', linestyle='--')\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(residuals, bins=15, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean Absolute Error (MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 6.11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(f'Mean Absolute Error: {mae:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Multiple Values at Once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\venki\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = [[i] for i in range(1, 10)]\n",
    "predicted_scores = model.predict(hours)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(hours, predicted_scores, color='blue', linewidth=2, label='Predicted Scores')\n",
    "plt.scatter(x, y, color='red', label='Actual Data')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.legend()\n",
    "plt.title('Prediction of Scores for Different Study Hours')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}