Linear-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "opponent-baltimore",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination :  0.7305250573072464\n",
      "slope :  [0.50762887]\n",
      "intercept :  6.18144329896907\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "def abline(slope, intercept):\n",
    "    \"\"\"Plot a line from slope and intercept\"\"\"\n",
    "    axes = plt.gca()\n",
    "    x_vals = np.array(axes.get_xlim())\n",
    "    y_vals = slope * x_vals + intercept\n",
    "    plt.plot(x_vals, y_vals, '--')\n",
    "\n",
    "x = np.array([5, 15, 25, 35, 45, 60]).reshape((-1, 1))\n",
    "y = np.array([5, 20, 14, 32, 22, 38])\n",
    "x_plot = []\n",
    "y_plot = []\n",
    "for i in x :\n",
    "    x_plot.append(i[0])\n",
    "reg = LinearRegression().fit(x, y)\n",
    "print('coefficient of determination : ' ,reg.score(x, y))\n",
    "print(\"slope : \" ,reg.coef_)\n",
    "print(\"intercept : \" ,reg.intercept_)\n",
    "\n",
    "plt.plot(x_plot,y,'o')\n",
    "abline(reg.coef_,reg.intercept_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sustainable-smart",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination :  0.24456688961685802\n",
      "slope :  [74.14307112]\n",
      "intercept :  -23.01449070317839\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "def abline(slope, intercept):\n",
    "    \"\"\"Plot a line from slope and intercept\"\"\"\n",
    "    axes = plt.gca()\n",
    "    x_vals = np.array(axes.get_xlim())\n",
    "    y_vals = slope * x_vals + intercept\n",
    "    plt.plot(x_vals, y_vals, '--')\n",
    "\n",
    "inline = open('./data/train.pkl','rb')\n",
    "data = pickle.load(inline,encoding='bytes')\n",
    "temp_x = []\n",
    "temp_y = []\n",
    "for i in data:\n",
    "    temp_x.append(i[0])\n",
    "    temp_y.append(i[1])\n",
    "\n",
    "x= np.array(temp_x).reshape(-1,1)\n",
    "y = np.array(temp_y)\n",
    "\n",
    "reg = LinearRegression().fit(x, y)\n",
    "print('coefficient of determination : ' ,reg.score(x, y))\n",
    "print(\"slope : \" ,reg.coef_)\n",
    "print(\"intercept : \" ,reg.intercept_)\n",
    "\n",
    "plt.plot(temp_x,temp_y,'o')\n",
    "abline(reg.coef_,reg.intercept_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "christian-fisher",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination :  0.24456688961685802\n",
      "slope :  [74.14307112]\n",
      "intercept :  -23.01449070317839\n",
      "Bias :  18579.260015132386\n",
      "Avg Prediction :  -177.8159553287809\n"
     ]
    },
    {
     "data": {
      "image/png": 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jR0SH9KOrOxFIWMSiwluHB4oa8Lu6E7x12L8Mai4/eGpXVjCnVHlwY4Llq7cGEr59ZTg6NAONUBfDBEaDE2auprDzChVbyXsFjOWO54ZVm0pSHbmTWX6eHxjq3lpIsPjRHosiQsVRvLmTeSHhteO1g1xw+gReWHHpkFTjxZ7VkoWziEW8l9L5E3aQXc6hoyluWzyHJQtnccvDW5m6dA3X+0RJ5zOQ1qxazsXr2a9cu33IeX7kn9afTAVWM00s4E3VzDRCpLeppBqYcoOq/OwKYeVncu8f5Kuf+yEv1xgNxyZLdxxeY4RjAWGdHTEOJ1OBjZ8RySTwuyGgcboQh44OMHXpmqwKrZB//93rd3HP+l1DnkWxZ7VobrxgnEGuaizIbknJxKy8dWRgSBxJ0Wt9Ts+f4GrludSKHlKQUfvVGxMYDUw5hs1iQsbLEFmK4brUST9Xtx0pYoPIJRYRxoxqo+9Q0rNP+ePI71ep8ReuQTGMgjjua7tjLTbi/Ep4uUKhkNtysTG6E3Yx209+v8NiXM5OsKs7UXFgXESG7jyGE4frkL8qHxMYdSDoBF2OkbqcimSl7GJKMeLGosKBQ8khE2gQkmmlY0Qb3f98caDzK61LkEwr16/axPiOGLGIFFSdBJ2ASyWIl1MpAlskc369Ul28cfhYlt6gO9JCDGdhAZkAw3pjNowaU0qRHT/9t0jGf37q0jXMueXRQdeWKmRKTVYXRI/qGk/bIkIlH/FSdLZh6Xf3H0qCQEeBsphpVW5fPKcsO0kxio2jFMGY1oxX0fg6qTLc1+/qTpT1fAo9A6M+2BOpMaVM0H7G29yVVl9/kiX3b84KjVI9oUoVMMU8kNzgtyeWXljximhiZ3tgo3+YuaqSKeXIgP9y1o3X8Cq0Uyl+43Dfh1LVZf3JlKcqIwI1ESTuZ7uc5zOyCgK5mamX4M/FBEaNKWWCzs1dVIhkWrMCp1RPKL8v8rj2mOdEXcwDyZ1MK6U9FuWC0ycE3o0V61dUBCGjBw9CMTWOW2eiFJ//0SOO9a8jFhni5eT3nG7q2sINqzaVbVvxFNwCl551Sk2CwXb39ZfluRa2TaWZiUWFmy+bXe9umMCoNaXuABbNjfPE0guLCg1X4ASpmZCL1xc5FhEOHvX2qw8ixNy+lLsicvvsVxHNSyC5/fJ6zfZYlC//+dm8sOJSvvLnwVRJQabR/mSqpEnt4NFjY1GExedO9n1O7o5i6tI13B0gFqJU0popIlQL+4YIbHhpH6NMxVQ2i989uSHS99gTrDHlxkIU0wHnChxXyHj59+fjJWDGjGob4lqZO1EXE2JuXy4965Qhx2JRKShIct1nS9mNuY4EfU5qiNy56chAig0v7cv2/UPnxIsKhGpPo24p0CULZ9HZESPR18/1qzYx55ZHualrS03qRdfKhpzWjPuw7RjKZ82ze+rdBcAERs0pdQfgUkgHXElxFS+PLb8vdv5EXazi2oMbB6uOhMxK6ebLZvuu8vuTKZav3sqCFet8J7Tc96KrO8Hczz3K9Y7KJhu9nKOFcScsNz3049v2hjZZtscigXYjXrjFnHLf777+JHev31WXEqCV0KLZOBqGRhG25lZbB8qpVe1VvAcyuvB/ufKssrarXi61S+7f7Ht+vtAqteKakpms5516XEFbQl9/smBU76GjA1k7RikxIXev38W8U48LNWL2aErLFj5RkZKD5BoVt+RpI1araxVclXA9sfTmTUS5mWH9rivF6yY/9XcxCqXNLlYXIWh/yvnkVhI8FotAA7jCA7Bg+nH8ZldfQ/jm59LIVeuaHa8U8tXA0psPI7zKgD64MeEZmFfKSlvxT0eS/5pTjy9soA9D3VLupBT0Oq/gvUaam198vZ9RsWhZAqOzPcYbh5MlB8KNHRnljSOFn50Ji+rRCClPTGA0CUEisr3O8co2musbH/RD6Gfgzk9/nejrb4gPdiVEhMBJ8upFue+x6wFXzvCKCQujujRCPQwzejcYfoFqQQL+CqUbz8f1jQ/yASgUH1ANl8960+CyoiKSaW0Zu8lwoxHsQ7bDqCFBajT77SKCuJiWomJyDdh+Co2oCGlVz7rarVoz2TAamUZI2W4Co0YEUSkV2kX4qY/yay8Emchj0YwbbqGI7LQqL+TVD64kPblhGJVRzDZYC0wlVSOCqJQK7SIuOH2C57Hc9gtOnxDIHz6Z0qK7BK+4j0ozwubSHouGnocpn/prfA0jPNY/v7/eXai+wBCRF0Vki4hsEpENTttxIvKYiOxwfo/POX+ZiPSIyHYRWZjTfo5znx4R+ZpIA1iASiCISqlQ2pDHt+31PPb4tr10dSeYc8ujJdkTiu1EvGwWYcUvuMGKQSuolUv9Nb6GER6NYMOo1Q7jAlWdk+PbuxT4harOBH7h/I+InAFcDcwGLgG+KSJuSPC3gGuBmc7PJTXqeygEySFVKHLab7J2U0qEPflev2oT05c9wtQc43vHiMqzhwoZwbN89dbKO1kDBLh98RxuXzynIXTIhlFP6qWSugL4nvP394BFOe33quoRVX0B6AHOFZFTgLGq+qRmIg2/n3NNQ+Hn5RQkh1ShtCFhpu8OiruiSfT1c+P9mwclzysXdX6qvbsICzf77vWrNvHKgcP17o5h1JVaGL0VeFREFPi2qt4BnKSqewBUdY+InOicGwfW51zb67Qlnb/z2wchIteS2YUwZcqUsMdRlKCG7UKR2n5pQ/xSgxSjWORt0MjcVCv7mhbBVd81gkrAMOpJLQTGAlXd7QiFx0RkW4FzvewSWqB9cENGGN0BmdQg5XS2EoqVRy0nh5SLe93y1VtLWp0XexMslYNhGEGpukpKVXc7v18DfgScC7zqqJlwfr/mnN4LTM65fBKw22mf5NHeUJRTgzso2fTdAYVF0EJBDZTtwjCMBqeqAkNERovI29y/gYuB3wKrgWuc064Bfuz8vRq4WkRGisg0Msbtpx311ZsiMt/xjvpEzjUNQ9DiSEHLjuaeX0p9hFhUWjpa2TCGI43gdFFtldRJwI8cD9g24Aeq+jMReQa4T0Q+CewCrgJQ1a0ich/wHDAAXKeqro7nU8B3gXbgp85PQ+FlZ8g3bBeycwDc8vDWbO77zvYYyy+fXXL8g6V+MIzWo9yaN2Fi6c1Dplj6D7+U4h2xiOX5MQzDl9v9ygukkrDvBdi7Df6wHWZdCiedUfbrWHrzGlLMsO1nzzjUSLmzDcNoOL76s2dZdPLrMGosjJ8K+56HH34EXt8J6Rzb5ttOqUhgFMIERo0pJaV4IWIRATH1k2G0GhHSpIkQY4Ab2+5jpiSYIQkmH94L31b4bzfA+5fD6BPhuLfDrD+FCafDCadlfkaOqVrfTCVVRbzUU5CJoq4EATpGREMJpDMMo368S37P6ZGXmSm9TJfdzIgkeDJ9Bjcm/xZQnh55Ha/r29ipcV4ZOZW/XLQQ4u/K7DCqhKmk6oCfcfvWK8+ksz1WUaSzggkLw2gKlJPZx4zIbmZKLzNkN0eI8bmBTwBwa+w7zIr0clBH0qNxnkyfwa9T73SuFc478g3UcWa9/co58M761vQ2gVElCgXxLb98tqUJN4wWIkKaSbKXmdLLSdLHD1IXAXBH7CtcHN2YPa9PR/NU+h3Z/29I/i19OoY9HJcVDLnktpUb9BsmJjBCJFcF5afoS/T1s3Ltdt41ZRzrn99v6SYMo4mIMcCp8go7dSJKhI9Ef8Enoo/xdtnDSMloDVIqPJD6I44S48HU+/hV+ix6NE5POs4fGEtu4orndGqg122EGAwwgREapRQXaoW614YxHDhddnFpdH3W8HyqvEpMUiw4/FUSTGCAKHv0OP4rfSY7NM7O9ER6NM5RMrVe1qbPDaUfjRCDASYwQiPM4kKGYdSGURzhDHmJ6ZHdWaEwUxJ8Jnkdv9HTmCEJPhVdzUt6Ejt0Ej9Lv5uedJw3GA3A/anzuT91ftX72QjqKDCBERphFRcKk4/Nn8Ld63fVuxuGUWeUE3iDmRHHE0kS/Cx9LuvTZzBbXuTBkbcAcERj7NSJdOsMjjg7hEfT83jHke+SrPNU2dWdaAihYQIjJMKKrwhKZ3uMTTdfTFd3ghtWbfK0max5dk/N+mMY9UeZyOvMiCR4XceyVadxAgf4+cjP0ikHs2e9qe38XieznjP4nZ7KJ4/eyA6dRK9OIJ1neHZVS/Vm+eqtJjBaianH11ZgJFOZyPBFc+O+cR1uTirDaCWipBhDPwcYAyj/2nYHsyIvM112M0YyRa7uHTifpQPX8jpv4+HUe3heT2GHTqInPZFXOA7X8HyIUfwifU79BhOQRik4ZgIjBG7q2sITO/fV9DUPHk1xU9cW31rfhtEqXBDp5ix5npmRBNMlwdtlD+vTZ3BNcikgnBp5lTe0g/vTf0yPxtmRjvN7zVRDUCL874G/qO8AWggTGCHww6dersvrmn3CaAXaOcx0cYzOkYzROUKav0wuAeDa6BrOi/yOl3UCOzTOf6bPZlN6Rvb6xUf/uV5drxnjOxpDNWYCIwQslsIwijOOt5ghCWZEdjNVXuFLA1cDwhdjd3Fl9P8BkNQoL+jJPKen4hbb/EzyOg4wmiOMqGf368rNl82udxcAExihEBUxoWEYACgT6GNGZDe/Sc/kCCO4OrqOG9vuZ4IcyJ7VryO4Y+BS9jOWewYuYm1qHj0a5yU9iYG8aek1xtd6EIYPJjBC4CPnTTb1kDGsENJEUFJEOUNe5Jroo8yIZOIYxskhAC4/8nme1ens0eN5PDWHHRrP2Bg0TkJPyKa92KizrLB8EVau3W5eUq3CFxadyQt736q54dswasEYDvHeyNasOmmGJJghu7kh+besTb+bcXKQC6O/oSc9idXp92aFQo9mJrj/TJ/Nf6bPrvMomptGifMygRES9/zVe+jqTrB89daGcYEzjKCM5CjT5JVMRtXIbmZILz9NncdP0u/hROnjjhG3AZDQ49mZnsi96Qvo1QkAPJk+g3cf+fd6dr/lmWi5pFqTIwNWOc9oXEbTn90hvM5YfpmewyiO8NuRn6RNMp/dlAq79ER+LZk02y/pSVx+5PPs1IkcxGviEo82IywiWC6plsTySRmNQidvMl7e4gU9BYBvxL7GuyK/Z6IcU5uuTc3jl+k5HGYk/zqwmN16Aj0a5wU9eZBHUoooz+r0mo/ByNBIS1ATGCHSKHpGY/hxWeTXnBf5XdbGcIK8wbPpaVx+9IsAHNRRPJk+g53pOD06kR06iV16Yvb6O1KX1avrRgDM6F0GInIJ8FUgCnxHVVfUuUuDqHU+KWP4cBL7OCPyUrZq28xIgrEc5KKjXwZgYfQZ3hfZwg6dxM9T72KHxtmmU7LX/+PAtfXquhECjbIYbRqBISJR4N+APwF6gWdEZLWqPlffnh1jycJZVknPKBu3OM8MJ6PqzEiCf0heyxFG8Fdta/jLtp8C8Jp20pOeyLM6jSgpUkS5MfkpJ8Oq2RNaETN6l865QI+qPg8gIvcCVwANIzDcLeON9222QD7Dl5EczabZ/lX6LPp4G1dFf8m/tN1JTI4tNnr1BCZIH716Ij9IXcQjqfPo0Ym8wZgh9xzOUdDDgQtOn1DvLgDNJTDiQG7Spl7gvNwTRORa4FqAKVOmUGvcEq0mLAyAt3GINMJB2jlddrGkbRUzJMFk2UtEMp+R/3H0H/hleg7PpU/l26k/oyediWF4Xk+hn1HZez2vE+s1DKMBeHBjgnmnHld3O0YzCQyvvfagmVlV7wDuAJg3b15NZ+1SSrQarccYDnFF9NeOy2pGnXSy7Gdp8i+5N3UhA0SYKH/gWX07D6Xelw1sc72Ytuo0tg5Mq/MojEalP5lqCMN3MwmMXmByzv+TgN116ssQzKW21VFOZh8zI8fKeM6IJHgsdQ7/kfozIqT5Yuwu3tJR9OhEnki/kx3pON1OVtUencSfHv1SncdgNDONYPhuJoHxDDBTRKYBCeBq4L/Xt0vHaISHaVROhDST5bWsUHidsdyfOh9B+cXIzzJajgCwX8ewQ+O85QSyvcEY5h/++qDiPIYRJo1g+G4agaGqAyLyaWAtGbfau1R1a527lcVcapuLGANMlVcYz5s8re8A4I7Yl/njyLOMlGOpXX6emsv9qfNRIvxD8q95nbHsSMd5nbHkC4ZXOL6WQzCGGY1g+G4agQGgqo8Aj9S7H16YS21jMpKjWQ+iq6K/5P2R3zBDEpwqr9ImaRJ6PAuOfB2AZ9Nv5wU9mR6N05OOs1Mn8gajs/dak55fjyEYBkBDVNdsKoHRyCyaG2fDS/v44VMvk1JFJJMDJmUOUzVjmuzh3ZFtmcA26WVmJMEEDnDGkbtIEeV0ydR9/r1O4pH0eexIH8uoCvCN1Afr2HvDKEwjqL1NYIREV3eCBzcmsi61qmB7jbBRTuANZkZ6nZKemajn65PXsZdO/jTyNP8QW8VhjfG8TuQ36ZnsSMcZQZJ+onx+4ON8no/XexCGURZmw2ghzEsqPIQ0E3k9m2b70fQ8XtaTuDzya7424t+y572p7fRonLFykL3ayf2pP+Yn6fn06gTSTnEew2gF2mPRhshYawIjJBphu9hsREkxRV7jLR3FXsYzXRLcFvsmM2Q3HY43EsAfjo7jZT2J3+hMlic/kSnQk47zKuPJNTzvpdMqtxkthwjceuWZdY/BABMYoTEqFqE/2UiJiBsJBYRRHOFv2h7O5kqaJnsYKQP8a/LP+WZqEX06hj4dw73pC7JCoUcnsp+xAPTqiXw3dUl9h2IYNUaVhhAWYAIjFG7q2mLCwuGd8jynOVXbZkqC6U6+pOUD/4Mkbfx19Ce8quPp0Yn8Mj2HHp3IhvRpALzOOD6RXFbnERhG9Zl54mh69x9uOjW2CYwQ+OFTLxc/qYXo5M1sfeeZkuAN7eCrqQ8B8O8jbmeS/IGjGuVFPZnf6RS26lQgU4jnrCPfIWkfO2OY0/PaQT46fwqPb9vL7r5+Jna203foKAePDhUg4ztideihN/bNDYHWTDaoTKCPmZEEJ/AGq9PvBeDO2EouinZnz+rXEfwqfVbWJewzR6+jjzG8pCcx4PHxMmFhGBkl7ePb9vLE0guzbV3dCZY8sJlkji9+LCrcfNnsOvTQG/v2DnOENHF5nV7NRJH+9+gv+FD0V8yUBGPlEJARCg8fmY8SYU3qPJ5Mn5EJbtM4CT0ezfFI2qj19+QwjGYg31HGtVOsXLs9u+tYsnBWw9gvwATGsON02cVFkd8wI5JwbAy7aZejzDn8bfp4G+0c4ajG+HH6vdmMqjvScdTxRnoo/Ud1HoFhtA7Tlq4ZJBjcn0bFBEYIxBsoj9QIkk6K7d1Md4TCDEnwd8m/Y7tO4azITpbE7qNXT2BneiLr0++gR+MMEAXgztQHuDP1gTqPwjBaH1fxlOjrZ9lDW4DG8YbywwRGCNQjj9QYDmWinSMZ4fBI6lye1em8J/Ic3xuRSaOdUuElPYkejWejFR5OvYefpN7DoZziPIZh1JdGqXdRDBMYIbBobpz7N+ziiZ37Qr/3eN7IptneqXHi7OX+kbcwUY691hFtY6eewrOp6WxKT+fTR/+OHRrnRT15SOnOfhMUhtGQ5No03OqdjWbLMIEREpUJC6WdI/QziigpPtf2XWY4hXqOlzcBuHPgT/n8wMfZSydPps9gp1PKs0fj7NITSTkqpQOM4Sfp94QwIsMwaombKyq/emcjqaxMYITATV1bSjr/fZFneYe8xEynlOd02c2v0mdyXfJ6UkR5d2QbBxjNo6l5WW+k59KZGuVHiXFj8m+rMQzDMOqEkKl3sWDFOk97aKOorExghMAPnto16P8YA5wqr2TTbM+I7CZJG59N/g0An227j7Mjz/OqdtKTjvNg+n08kz49e/3FR1fWtP+GYdQPAd47/Tge3JgoaAdthHx1JjAq4egheH0Hl8n/Y2JkH99KXQ7A12Nf55LoM9nTXk5PoFtnZP//n8lPs1/H8AZjat5lwzAah6gIHzlvMo9v21vUacbSmzcL/X3wh9/DxHdBtA2e+Q488VXoexlQvjoCkhrlu6mL6WcU96Qu4mepd7ND4zyvpwwxNL+kJ9dlGIZhHCMWFaYe38GO1w7WrQ8p1aI7Cxcr0dqo7NkM3ffA3m2wdzu89Uqm/bpnYMJp0HE8THo3zP04nHAaf3L3q7yoJ2fTXvxX+qw6dt4wjGJEBJIpDU1YRCPC20a2caA/WXKG/f5kiqhI0RRDVqK1UTmQgE33wAmnwfQLM0LihFkw9pTM8dkfzPw47NA1deqoYRjlkA45/VsqrYwe2cammy9m7uceZf+hZGnXq9Iei5oNoyk5bSEs681ULgnA+I5YyR8QwzBaC3dCL7RRiIi3sIo7sRYr1273zRrRCDYMq2PpRSQaWFhA4Q+IYRjDA3dCP9Dvv3gcOypGeyw6qM0tv7pobpwnll7I7Yvn+J5Tb6omMERkuYgkRGST8/OBnGPLRKRHRLaLyMKc9nNEZItz7GsimVlbREaKyCqn/SkRmVqtfpdDoQ+IYRj1IVrCoq9SBLITeqGdwIH+JLdeeSbxznaEzM4iv/zqornxoufUi2qrpG5T1f+T2yAiZwBXA7OBicDPReQ0VU0B3wKuBdYDjwCXAD8FPgnsV9UZInI18CVgcZX7HpiJDZR80DCMDLWqUyPAR+dPyU7oSxbO4oZVmzyN3xM72wNlpG3UrLX1UEldAdyrqkdU9QWgBzhXRE4Bxqrqk6qqwPeBRTnXfM/5+wHgInf30Qg0wlbRMIzaE+9s57bFc/jCojOzbYvmxvno/CnkT1CNolaqhGoLjE+LyLMicpeIjHfa4kBuTdNepy3u/J3fPugaVR0ADgDH57+YiFwrIhtEZMPevbVzQdvwUvhJBw3DCI9qLS+fWHqh507gC4vO5LbFcxpSrVQJFamkROTngFcU2j+RUS99nkza988DXwb+AoYIXpxz/NopcuxYg+odwB0A8+bNq5kperjV9DaMZqNU7ZQAHSOinjW2XYrZSBpVrVQJFQkMVX1/kPNE5D+Anzj/9gKTcw5PAnY77ZM82nOv6RWRNmAc0DDL+tas6W0Y5SF4rOaaAHE6Pq49hghFXeU/ct5kzzTk0NhlViuhakZvETlFVfc4/34Q+K3z92rgByLyFTJG75nA06qaEpE3RWQ+8BTwCeDrOddcAzwJfBhY59g5GoIgUZqGMVxo1m+CKnS2x+gL4PW4YPpxzDv1uCFpyJc8sBkUkk6wRSOlJg+Datow/tVxkX0WuAC4AUBVtwL3Ac8BPwOuczykAD4FfIeMIXwnGQ8pgDuB40WkB/h7YGkV+10yHzlvcvGTDMOoiKgIH5s/hfEdsYLnlWuvEPAVFlGRrC3i9sVzuOev3sPKtduHRGYnU5oVFi5uavJWoGo7DFX9eIFjXwS+6NG+AXinR/th4KpQOxgiX1h0Jnev31X8RKPl+dj8KdyzflfTrrIblfZYlFuvzHgiPb5tb0F1UTmb/WJqtJQqL664dFBbKak6GiGtRxhYpLdhhICQERZfWHQm49oLr4CN0uhsj2WFxbKHtoQe8xTvbC8q4L0M3KWk6miEtB5hYAIjBLq6E/XuglFHxnfEsr74Xd0JDh4dqHeXWorRI9tYNDfuqQKqlHhnO08svZB4kQk9pcqCFesGfdeXLJw1JIVHLCrEIoOFSywiHDo6wLSla4bco9mw5IMhsHz11np3wagDcQ8PmJVrt5NMNY5CKirC/LeP58XX+0temXcG9BaqNq46J2y1TiwiWa+mJQtnDTJge5FvwHafeyEvqXHtMQ4eHci+h81uBDeBEQJBvCqM5iMWESIR4chAelC7q093v/C5rpWNIyoypFR5+oX9LD53cuBCPS7FPtdCRtXSMSJS1SJErjqnM+ys0DkbAfdZLl+9teC482tr+8VauG0LVqwbcr9Gqc9dDqaSMmpKM33gxoxq40sfOovbC0TsdnUnsnr1RhMWLsm0cvf6XZ7CotwA6HhnOy+suJQnll7I83sPVdbBAuSm0yhmzHa9qFz1UrGxJVM6yHtp0dw4m26+OPu8/QjD2N2sRnDbYRg1ZVxHDNXm2JXtP5Rk2UNbuPXKM3li6YWe51RDr15LlNLrueSqcqB6gatRET50zrEVfLGs0ClVHt+2N6smDLLz85q43V3DghXrPNV4pRq7K71HI2ECIwRGF0khYByj71CS2xbPKaovbhT6kyluvG8z16/alA3QjOfoqsP02OlsjzF6ZFuge8adiajSoFF3JV2Sqidv6V6twNWUKquefpk1z+6h71CSSIDX8bMzlDP5e9k1Sk0gGMY9Golm0hA0LLGovY1BmdjZ3nSrcneScn8n+vpZcv/mTFRvSLTHoiy/fDZPLL1wiL9/Pq7Aao9FK5qoXe+dUoVevipn/tvHFzi7MpJpZf+hTJ3soGP1CpTz8mgqNnGHUZeikWtblIPtMELACigFIxYVDh4ZqIk6qtrpWvKjeSslfxIp1H+3lGepQtfdwXh575RKrirnxddrq49335tC71G+qsnPo6kWdSlaKQmhCYwQGBcw/8xwJ5nSmrxP7gq8WdReUZEhE0ohYbdobpzrV20q6TXcHUwh751SiIjQ1Z1g0dx4zQ24aSfquqs7wY33bfZ8r7xUTa00cdcL06WEQOOUcqqcWJN/InLrI+eqAsZ3xIYEVHnhqg0ECHB6KHhNeH5eOm57KeVHx3fEhuxgKrW9pFRZ9tAWuroToRlwx3cMrXftxcTO9qx3mtd718w2gkanyaeHxqCvzoFNYZJMFz+n0XCnznz98KK5cZ5YeiEvrLiU7n++mJVXnU1HEYn40flTeGLphdy2eE7N0q56CYdiOvdS1G0dI9qGrKzDqHft2grCmpxvvmz2ICHv9azc98BPJRcVyaYRWbBiXUtEVzcSppIKAVNJ1ZbRI6J0dozw1EV3dSdYsGKd5zFXJXFT1xZ++NTLgybdqAgfOW9yttTm8tVbCSI7oyKkVensiPHW4YGybBsXnD5hSFsxnXspNhovlVFY9p2w1VHuM3J3ELkIZN1sb/BRyaWdceWnHc+9V6vWqqgFJjBCIJlqwmV5E3PwaIovfnCwkJj7uUeHGHD90jB8YdGZg2owexF0AZBW5QXHqym/mM6hgEblHz71MvNOPW7IxFVI517KhO+lMor7xAdAxnsqV/C1x6KMikW8xyKE5i22fPXWQYIyfwehZDLVQuH4Bq9r+5Mpbnl4K4eTaU9BYkIjGKaSCgGLwag9rv68qzvBkgc2+07M1a5FkDsZ56rAnlh6ITdfNjvQPXLtAUHpDJgR10+fv2ThLN9I6FhUhriB3nzZbE/7giqh5c7KFdLFIqQLqez8rt1/KOkpSFqlVkUtsB2GURPCdnPN/aIXm7DKUZsEiX72mozzdxntsQj9AQxDpeQXKpQRNxoR3jayjQP9yYIql0KeVoeSaf7F47oNL+2ruO5LeywayHOtWIR0IZXdyrXbSzLqJ/r6sx5fRmFMYIRA0LKOrYy74vV6H9wU0q5eOnfC8CtcE0TABBUE5dSnuPmy2Sx5YPMgYRSNCCOjwiFHAIxsG7xBzx9fqZ5IQcfjlxE3IvDlq84OPPEVUkvdeN9mbli1aVAG1gc3lmY4bo9F+dA5cR7ftnfQpH7Lw1s9hXFuJb0gEdJ+Kju/a0e2RXy/p6aaCoYJjBD4s7NPGdYV91wff6Dgl9xrVTj1+HZ+vXPfIKHhTjTFsqu6q81iE3M5DkFefb3g9AmDJs2+/uSgiaZQMJ0rGDvbY7xxOImXbTyoe6qfYFEtbcJbsnCW7y4jN6p92UNbGBWLlBTT0tkeGxT3MeS184RxLCqDVHjlBtoVuhaGfj5dmjmDbC0xgRECP9m8p95dqAtuemuvmhB+X/LcVaG7Ite8e37onDhfWHQm8049LqteyN+J5Aqi/Mknn0Juz/kqJC+vKpcFK9b56sCLBbAphXdapcQOhJXQbtHcuO9qP5f+ZKqgsIhFhDGj2ug7VFgNlvu6UFwYVBJoV+haPyHZrBlka4kJjBAYjuooEbjtz+eU/CXPnaC9kskpGQGcq8a4ffEcoPAEU2ji85tIvVRIhVQTxQyxfhN5/nmVrJ4h3IR2N182u6KI+IjAyhLUYC71irouZONo1gyytcQEhlEWqv56X79Ve/4E7Wej6OtPZoWwO4kXSjGe77sfZCL1SytRSDXhJxDGtcey2VD9bDLu9fl9LodKBU6he/llhPUb19hRsaZT47RaBtlaUpHAEJGrgOXAO4BzVXVDzrFlwCeBFPA/VXWt034O8F2gHXgE+IyqqoiMBL4PnAO8DixW1Reda64BbnJu/QVV/V4l/TbCwWtyLbRqLzdLbVD9cqGJNFeIuYn3giauc/HLT5Ur4PyERdgTUpgrdC81Yf5k6vfcmjHxZpgCd7hR6Q7jt8CVwLdzG0XkDOBqYDYwEfi5iJymqingW8C1wHoyAuMS4KdkhMt+VZ0hIlcDXwIWi8hxwM3APDLfx40islpV91fY92FJoRVwObiTqzshe63A3Qm/Eh1x0Gu9JtL8SbCYCtFPNbFobpwNL+3jnvW7ir6H4ztidIxoq/uEVMhG44XfZNpqahxLRFgeFQkMVf0dgAx1Q7kCuFdVjwAviEgPcK6IvAiMVdUnneu+DywiIzCuILNbAXgA+IZkbrwQeExV9znXPEZGyPywkr6Hyci2yJC6z41AvmtqbvoLv4IyXhSK9M1NBFdo9+BOPoVeMyrC2PY239cpl1J2NsV2Ao9v2xtI4PYdStL9zxcH7GF1KGaj8RMmfpNpLdQ4uQuP/IJVNsHXn2pFeseBl3P+73Xa4s7f+e2DrlHVAeAAcHyBew1BRK4VkQ0ismHv3r0hDCMYQTJs1oN8lUtKlQc3OtHRHpGyXrheS16RvsUSweXiTkiFXjOtWvB1yiXo7sRNXFdoYgp6r0ZYefulyFi5dnvmM3D/5mwtcrcolF+0eS0KAeXWR4ehrr2WQLD+FN1hiMjPgZM9Dv2Tqv7Y7zKPNi3QXu41gxtV7wDuAJg3b16Nco02lx7XnTBcA3KxqFg3f4+be8lrReqXCM4lN+U4ULCGQTX0y8V2Nm4fg0yAQe4l0BAG1EJeXctXbx2SKDGZ1kH5nPKpthqn0MLD4iQag6ICQ1XfX8Z9e4HJOf9PAnY77ZM82nOv6RWRNmAcsM9pPz/vml+W0aeq0RkgjUQjsdtJheBOysWiqnPdQUvxHgKGqBPc38UC/MKcGLyM1aXGDhS6Vy5CJkV6rSc2L/VSoXgNv+dVTxfxYrs3i5OoP9Vyq10N/EBEvkLG6D0TeFpVUyLypojMB54CPgF8Peeaa4AngQ8D6xzvqbXAv4iIWzj4YmBZlfpdFlWsBFoVxrXHArm3uhRTr/i5Kfqt2GvtpVJNN9Rx7TFEKFnwhImfrcIrWt4VzMUq9pVqLA+DYru3RlDzDXcqdav9IJkJfwKwRkQ2qepCVd0qIvcBzwEDwHWOhxTApzjmVvtT5wfgTuD/OgbyfWS8rFDVfSLyeeAZ57zPuQbwRqHZAveODqQ8E+KJDBV+QewH5UzIft5M1ZqkquWG2gj42Soe37aXW6880/M9LZTPqdSAxrAotHuzOInGQLTZlscBmTdvnm7YsKH4iSEwfdkjRVfpo0dEfdOgRyBQsZ5qI8Bti+fUxT/dz/8/bMNqKzJt6RpPo55AtlZHPm5a+Px8Tis/fLavXctNbVJNzEuq/ojIRlWd53XMIr1DIEja7kI1MxpBWMAxo3M9vpiFPHpsoihMObmlCu0K/ZwYamFDaLTdmzEYExghEHath2ozviM2qPIYBN/yV0ttVCxPk+FPuakuSnViMBuCYQIjBKotLIpFZxcq9uOV5dVNIx104s9VE+TeL0zdtk1S5RO2E4HlWjL8sBKtIRCv8qQ2sbN9UHGZXMZ3xAqWAnXTapcbbJUfTJUvuMIqcVmo5KZRnEVzB5eHrUSA1yJIz2hObIcRAhecPqGqBZQSff3EIkIsKoOMlO5uoZDXi5ehshQvmCBR3GGojUpdJdfD7XM4YbYEwwsTGCHw+LbqpyFJppXO9hijR3ontPOqa+C3Qi/FwBxEGISlNgo6SdXL7XO4YMLY8MMERgiEscJ2VUuL5sZ93SQP9CfZdLN3QrtSVuilGJiLBVPVQ21kHlXVw4SxUQgTGCEQJL9QMQ7nBNKVawAOukIv5f5eBlDX8F0v/3jzqKoeJoyNQpjROwSWLJxFLOKVIzE4/ckUy1dvHVS5LZcwV/KlGJi9DKC3LZ7DiyEYV8vFT3CaR1XlmDA2CmE7jBAoZHQuhfzKbdVayZdqYG40A6i5fVYPc282CmECIyT6qpCt1hUW1UjH0GhCoBSsxGb1MGFsFMIERkiEYcfwohr3bAWaWeA1MiaMjUKYwAiJJQtnDUnmFgbRoeVvDaOqmDA2/DCBESJBhIUbFR505+CmHWlW3/hm7bdhGEMxgRECbn3kYuTqgv1cVfPpbI8x55ZHB9XcaBbfePPpN4zWwtxqQ2Dl2u1D6iPnM74jls3H4+Wq+tH5U4a4usYiwsGjA54FmsLK4VRNCvn0G4bRfNgOIwSCqJc6RrQNWlXn6oldtU1/MjWoaMyhowMFXXUb3TfefPoNo7WwHUYIBDFM+02S+dlgU6pZ1VUxV91G9423ADvDaC1MYIRAkHoYfpNkIbVNoYm1GXzjLWW5YbQWJjBCoFg9jFhEuOD0CSxYsY5pS9ewYMU6uroTQGG1jdeEC4PtIY2M1VUwjNbCbBgh4BUdm8uItggPbkx4egsVSsXQCkFU5tNvGK2DCYwQcCfE61dt8jx+8OhQQeKqnYqlYrAJ1zCMRqEilZSIXCUiW0UkLSLzctqniki/iGxyfv4959g5IrJFRHpE5GsiGYuxiIwUkVVO+1MiMjXnmmtEZIfzc00lfa4Wi+bGSy7Vuruv39Q2hmE0DZXuMH4LXAl82+PYTlWd49H+LeBaYD3wCHAJ8FPgk8B+VZ0hIlcDXwIWi8hxwM3APDKxbRtFZLWq7q+w76Hjt1sY2RbxjKVwjdq2izAMoxmoaIehqr9T1cBRWCJyCjBWVZ9UVQW+DyxyDl8BfM/5+wHgImf3sRB4TFX3OULiMTJCpuHw2y0sv3y2eQsZhtH0VNOGMU1EuoE3gJtU9b+AONCbc06v04bz+2UAVR0QkQPA8bntHtcMQkSuJbN7YcqUKeGNpAQK7Raa2XhtGIZRVGCIyM+Bkz0O/ZOq/tjnsj3AFFV9XUTOAbpEZDYMKSQHx1Io+R0rdM3gRtU7gDsA5s2bF27a2AoxtZNhGM1OUYGhqu8v9aaqegQ44vy9UUR2AqeR2R1Myjl1ErDb+bsXmAz0ikgbMA7Y57Sfn3fNL0vtUyNimVwNw2gmqhK4JyITRCTq/P12YCbwvKruAd4UkfmOfeITgLtLWQ24HlAfBtY5do61wMUiMl5ExgMXO21NTW5KEOVYbIYb0GcYhtFoVOpW+0ER6QXeA6wREXci/yPgWRHZTMaA/Tequs859ingO0APsJOMhxTAncDxItID/D2wFMC57vPAM87P53Lu1bRYJlfDMJqNiozeqvoj4Ece7Q8CD/pcswF4p0f7YeAqn2vuAu6qpK+NhmVyNQyj2bBcUnXCMrkahtFsmMCoE5bJ1TCMZsNySdWJVkgsaBjG8MJ2GHXCXGoNw2g2bIdRB1yXWq905yY0DMNoVGyHUQfMpdYwjGbEBEYdMJdawzCaERMYdcBcag3DaEZMYNQBc6k1DKMZMaN3HTCXWsMwmhETGHXC0p0bhtFsmErKMAzDCIQJDMMwDCMQJjAMwzCMQJjAMAzDMAJhAsMwDMMIhGSqoLYeIrIXeAk4AfhDnbtTLVp5bGDja3ZsfM3Jqao6wetAywoMFxHZoKrz6t2PatDKYwMbX7Nj42s9TCVlGIZhBMIEhmEYhhGI4SAw7qh3B6pIK48NbHzNjo2vxWh5G4ZhGIYRDsNhh2EYhmGEgAkMwzAMIxAtKzBE5O9EZLuIbBWRf81pXyYiPc6xhfXsY6WIyGdFREXkhJy2ph+fiKwUkW0i8qyI/EhEOnOONf34AETkEmcMPSKytN79qRQRmSwij4vI75zv3Gec9uNE5DER2eH8Hl/vvpaLiERFpFtEfuL83zJjC0pLCgwRuQC4AjhLVWcD/8dpPwO4GpgNXAJ8U0SivjdqYERkMvAnwK6ctlYZ32PAO1X1LOD3wDJonfE5ff434E+BM4CPOGNrZgaAG1X1HcB84DpnTEuBX6jqTOAXzv/NymeA3+X830pjC0RLCgzgU8AKVT0CoKqvOe1XAPeq6hFVfQHoAc6tUx8r5TbgH4Bcr4WWGJ+qPqqqA86/64FJzt8tMT4yfe5R1edV9ShwL5mxNS2qukdVf+P8/SaZiTVOZlzfc077HrCoLh2sEBGZBFwKfCenuSXGVgqtKjBOA94nIk+JyH+KyLud9jjwcs55vU5bUyEilwMJVd2cd6glxpfHXwA/df5ulfG1yjg8EZGpwFzgKeAkVd0DGaECnFjHrlXC7WQWaOmctlYZW2CatuKeiPwcONnj0D+RGdd4MlvjdwP3icjbAfE4vyH9iouM738BF3td5tHWdONT1R875/wTGVXHPe5lHuc35PiK0CrjGIKIjAEeBK5X1TdEvIbaXIjInwGvqepGETm/zt2pK00rMFT1/X7HRORTwEOaCTJ5WkTSZBKF9QKTc06dBOyuakfLxG98InImMA3Y7HwZJwG/EZFzaYHxuYjINcCfARfpsWChphlfEVplHIMQkRgZYXGPqj7kNL8qIqeo6h4ROQV4zf8ODcsC4HIR+QAwChgrInfTGmMriVZVSXUBFwKIyGnACDJZJVcDV4vISBGZBswEnq5XJ8tBVbeo6omqOlVVp5KZfN6lqq/QAuODjAcR8I/A5ap6KOdQS4wPeAaYKSLTRGQEGUP+6jr3qSIks3q5E/idqn4l59Bq4Brn72uAH9e6b5WiqstUdZLzfbsaWKeqH6MFxlYqTbvDKMJdwF0i8lvgKHCNs0rdKiL3Ac+RUXVcp6qpOvYzVFS1Vcb3DWAk8Jizi1qvqn/TKuNT1QER+TSwFogCd6nq1jp3q1IWAB8HtojIJqftfwEryKiEP0nGo++q+nSvKrTy2Dyx1CCGYRhGIFpVJWUYhmGEjAkMwzAMIxAmMAzDMIxAmMAwDMMwAmECwzAMwwiECQzDMAwjECYwDMMwjED8fxDPblsvXjEnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "def abline(slope, intercept):\n",
    "    \"\"\"Plot a line from slope and intercept\"\"\"\n",
    "    axes = plt.gca()\n",
    "    x_vals = np.array(axes.get_xlim())\n",
    "    y_vals = intercept + slope * x_vals\n",
    "    plt.plot(x_vals, y_vals, '--')\n",
    "\n",
    "inline = open('./data/train.pkl','rb')\n",
    "data = pickle.load(inline,encoding='bytes')\n",
    "inline2 = open('./data/test.pkl','rb')\n",
    "data2 = pickle.load(inline2,encoding='bytes')\n",
    "temp_x = []\n",
    "temp_y = []\n",
    "temp_test_x = []\n",
    "temp_test_y = []\n",
    "for i in data:\n",
    "    temp_x.append(i[0])\n",
    "    temp_y.append(i[1])\n",
    "\n",
    "for i in data2 : \n",
    "    temp_test_x.append(i[0])\n",
    "    temp_test_y.append(i[1])\n",
    "\n",
    "# print(temp_test_x)\n",
    "# print(temp_test_y)\n",
    "\n",
    "x = np.array(temp_x).reshape(-1,1)\n",
    "y = np.array(temp_y)\n",
    "\n",
    "test_x = np.array(temp_test_x).reshape(-1,1)\n",
    "\n",
    "reg = LinearRegression().fit(x, y)\n",
    "print('coefficient of determination : ' ,reg.score(x, y))\n",
    "print(\"slope : \" ,reg.coef_)\n",
    "print(\"intercept : \" ,reg.intercept_)\n",
    "\n",
    "predicted_data = reg.predict(test_x)\n",
    "bias = 0\n",
    "avg_prediction = 0\n",
    "\n",
    "# print(len(predicted_data))\n",
    "# print(len(temp_test_y))\n",
    "\n",
    "for i in range(0,len(temp_test_y)) : \n",
    "    bias += predicted_data[i] - temp_test_y[i]\n",
    "    avg_prediction += predicted_data[i]\n",
    "\n",
    "avg_prediction = avg_prediction/len(temp_test_y)\n",
    "\n",
    "print(\"Bias : \", bias)\n",
    "print(\"Avg Prediction : \",avg_prediction)\n",
    "\n",
    "plt.plot(temp_x,temp_y,'o')\n",
    "abline(reg.coef_,reg.intercept_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "younger-worker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination :  0.24456688961685802\n",
      "slope :  [74.14307112]\n",
      "intercept :  -23.01449070317839\n",
      "Avg Prediction :  -177.8159553287809\n",
      "Variance :  4772464.112267932\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import pickle\n",
    "import math\n",
    "\n",
    "def abline(slope, intercept):\n",
    "    \"\"\"Plot a line from slope and intercept\"\"\"\n",
    "    axes = plt.gca()\n",
    "    x_vals = np.array(axes.get_xlim())\n",
    "    y_vals = intercept + slope * x_vals\n",
    "    plt.plot(x_vals, y_vals, '--')\n",
    "\n",
    "inline = open('./data/train.pkl','rb')\n",
    "data = pickle.load(inline,encoding='bytes')\n",
    "inline2 = open('./data/test.pkl','rb')\n",
    "data2 = pickle.load(inline2,encoding='bytes')\n",
    "temp_x = []\n",
    "temp_y = []\n",
    "temp_test_x = []\n",
    "temp_test_y = []\n",
    "for i in data:\n",
    "    temp_x.append(i[0])\n",
    "    temp_y.append(i[1])\n",
    "\n",
    "for i in data2 : \n",
    "    temp_test_x.append(i[0])\n",
    "    temp_test_y.append(i[1])\n",
    "\n",
    "# print(temp_test_x)\n",
    "# print(temp_test_y)\n",
    "\n",
    "x = np.array(temp_x).reshape(-1,1)\n",
    "y = np.array(temp_y)\n",
    "\n",
    "test_x = np.array(temp_test_x).reshape(-1,1)\n",
    "\n",
    "reg = LinearRegression().fit(x, y)\n",
    "print('coefficient of determination : ' ,reg.score(x, y))\n",
    "print(\"slope : \" ,reg.coef_)\n",
    "print(\"intercept : \" ,reg.intercept_)\n",
    "\n",
    "predicted_data = reg.predict(test_x)\n",
    "bias = []\n",
    "avg_prediction = 0\n",
    "variance = 0\n",
    "\n",
    "# print(len(predicted_data))\n",
    "# print(len(temp_test_y))\n",
    "\n",
    "for i in range(0,len(temp_test_y)) : \n",
    "    # bias.append(predicted_data[i] - temp_test_y[i])\n",
    "    avg_prediction += predicted_data[i]\n",
    "\n",
    "avg_prediction = avg_prediction/len(temp_test_y)\n",
    "\n",
    "for i in range(0,len(temp_test_y)):\n",
    "    bias.append(avg_prediction - temp_test_y[i])\n",
    "    variance += math.pow((predicted_data[i] - avg_prediction),2)\n",
    "\n",
    "variance = variance/len(temp_test_y)\n",
    "# print(\"Bias : \\n\", bias)\n",
    "print(\"Avg Prediction : \",avg_prediction)\n",
    "print(\"Variance : \", variance)\n",
    "\n",
    "plt.plot(temp_x,temp_y,'o')\n",
    "# plt.plot(temp_test_x,temp_test_y,'o')\n",
    "abline(reg.coef_,reg.intercept_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "solid-castle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[6;30;46mMean Bias : \u001b[0m\n",
      " [231.88467500877186, 229.22306640050337, -10.20989274898387, -3.977737370428767, -2.718472860362872, -4.077755369441147, -2.4663118044181465, -8.693289366407644, -7.885010333182945, -9.039902478010626]\n",
      "\u001b[6;30;46mSquared Mean Bias : \u001b[0m\n",
      " [53770.502503923744, 52543.21417004958, 104.24190994575342, 15.822394588105562, 7.390094692529494, 16.628088853006105, 6.0826939166122935, 75.57328000809622, 62.17338795440181, 81.71983681194266]\n",
      "\u001b[6;30;46mMean Variance : \u001b[0m\n",
      " [271042.02658211335, 526348.9297993318, 558656.6288151158, 859127.8866086949, 1234635.7435099867, 1412810.359223908, 1606993.9698098751, 1715771.0474544503, 2266649.0933583984, 2205113.8871433726]\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import numpy as np\n",
    "import pickle\n",
    "import random\n",
    "import math\n",
    "\n",
    "def abline(slope, intercept):\n",
    "    \"\"\"Plot a line from slope and intercept\"\"\"\n",
    "    axes = plt.gca()\n",
    "    x_vals = np.array(axes.get_xlim())\n",
    "    y_vals = intercept + slope * x_vals\n",
    "    plt.plot(x_vals, y_vals, '--')\n",
    "\n",
    "inline = open('../Linear-Regression/data/train.pkl','rb')\n",
    "data = pickle.load(inline,encoding='bytes')\n",
    "inline2 = open('../Linear-Regression/data/test.pkl','rb')\n",
    "data2 = pickle.load(inline2,encoding='bytes')\n",
    "var = list(range(len(data)))\n",
    "random.shuffle(var)\n",
    "packets = len(data)/10\n",
    "temp_x = []\n",
    "temp_y = []\n",
    "temp_test_x = []\n",
    "temp_test_y = []\n",
    "resampled_x = []\n",
    "resampled_y = []\n",
    "temp_kx = []\n",
    "temp_ky = []\n",
    "z = 0\n",
    "for i in var:\n",
    "    if((z+1)%packets == 0):\n",
    "        temp_kx.append(data[i][0])\n",
    "        temp_ky.append(data[i][1])\n",
    "        resampled_x.append(temp_kx)\n",
    "        resampled_y.append(temp_ky)\n",
    "        temp_ky = []\n",
    "        temp_kx = []\n",
    "    else:\n",
    "        temp_kx.append(data[i][0])\n",
    "        temp_ky.append(data[i][1])\n",
    "    z += 1\n",
    "\n",
    "for i in data:\n",
    "    temp_x.append(i[0])\n",
    "    temp_y.append(i[1])\n",
    "\n",
    "for i in data2 : \n",
    "    temp_test_x.append(i[0])\n",
    "    temp_test_y.append(i[1])\n",
    "\n",
    "x = np.array(temp_x).reshape(-1,1)\n",
    "y = np.array(temp_y)\n",
    "\n",
    "temp_test_x2 = np.array(temp_test_x).reshape(-1,1)\n",
    "\n",
    "# reg = LinearRegression().fit(x, y)\n",
    "# print('coefficient of determination : ' ,reg.score(x, y))\n",
    "# print(\"slope : \" ,reg.coef_)\n",
    "# print(\"intercept : \" ,reg.intercept_)\n",
    "\n",
    "# polynomial part\n",
    "\n",
    "# plt.plot(temp_x,temp_y,'o')\n",
    "\n",
    "mean_bias = []\n",
    "mean_variance = []\n",
    "total_polynomials = 10\n",
    "\n",
    "for i in range(1,total_polynomials+1):        # Polynomial loop\n",
    "    poly_bias = []\n",
    "    val_mean = 0\n",
    "    val_variance = 0\n",
    "    bias = [0 for k in range(1,total_polynomials+1)]\n",
    "    for j in range(0,10):       # test case loop\n",
    "        abc = np.array(resampled_x[j]).reshape(-1,1)\n",
    "        poly_reg = PolynomialFeatures(degree=i)\n",
    "        X_poly = poly_reg.fit_transform(abc)\n",
    "        poly_reg.fit(X_poly,y)\n",
    "        lin_reg = LinearRegression().fit(X_poly,resampled_y[j])\n",
    "        # plt.plot(temp_test_x2, lin_reg.predict(poly_reg.fit_transform(temp_test_x2)),'o')\n",
    "        predicted_value = lin_reg.predict(poly_reg.fit_transform(temp_test_x2))\n",
    "        poly_bias.append(predicted_value)\n",
    "        for k in range(0,len(predicted_value)):\n",
    "            val_mean += predicted_value[k]\n",
    "    val_mean = val_mean/10\n",
    "    for k in  range(0,len(temp_test_y)):\n",
    "        val_mean -= temp_test_y[k]\n",
    "    val_mean = val_mean/len(temp_test_y)\n",
    "    mean_bias.append(val_mean)\n",
    "    # variance finding down\n",
    "    for j in range(0,len(poly_bias[0])):\n",
    "        temp = 0\n",
    "        for k in range(0,10):\n",
    "            temp += poly_bias[k][j]\n",
    "        temp = temp/10\n",
    "        for k in range(0,10):\n",
    "            val_variance +=  math.pow((poly_bias[k][j] - temp),2)\n",
    "    val_variance = val_variance/len(poly_bias[0])\n",
    "    mean_variance.append(val_variance)\n",
    "\n",
    "squared_mean_bias = [ i*i for i in mean_bias]\n",
    "print(\"\\x1b[6;30;46m\"+\"Mean Bias : \" + \"\\x1b[0m\" + \"\\n\",mean_bias)\n",
    "print(\"\\x1b[6;30;46m\"+\"Squared Mean Bias : \" + \"\\x1b[0m\" + \"\\n\",squared_mean_bias)\n",
    "print(\"\\x1b[6;30;46m\"+\"Mean Variance : \" + \"\\x1b[0m\" + \"\\n\",mean_variance)\n",
    "plt.plot([1,2,3,4,5,6,7,8,9,10],squared_mean_bias,'o')\n",
    "plt.plot([1,2,3,4,5,6,7,8,9,10],mean_variance,'o')\n",
    "# plt.plot(temp_test_x2, lin_reg.predict(poly_reg.fit_transform(temp_test_x2)),'o', color='red')\n",
    "# abline(reg.coef_,reg.intercept_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "composite-insulin",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
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}