Lecture_5_2_LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REGRESI LINIER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18e62e49278>"
      ]
     },
     "execution_count": 36,
     "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": [
    "import numpy as np \n",
    "from matplotlib import pyplot as plt\n",
    "x = np.array([[0.0],[1.0],[2.0]])\n",
    "y = np.array([1.0,2.0,2.9]) \n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = [0.95] ; dan b = 1.016666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression().fit(x, y) \n",
    "print(\"m =\",lm.coef_, \"; dan b =\", lm.intercept_)\n",
    "#lm.coef_ # m array([ 0.95]) \n",
    "#lm.intercept_  # b 1.0166666666666671 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18e62d359e8>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cffuhcXfPCv7dYmbTgSHAj4I/eCcwBSApKclL+XOIxL0D+QU8/mEGT328ksZ1a/LYJYMYM6Ctvm5BfiSSo3oMeA5Y6u6PlDAnAZgGXObuy8PG6wPVgs8G6gMjgclRqVxE/mPBmh1MSE5h5da9XDyoPbed14em6nsrJYhki384cBmQamaLgrFbgAQAd38auB1oDjwZbF0cOmyzNTA9GKsBvOru70X1JxCJY3sO5PPge8v45/y1tGtclxevOoER6nsrRxDJUT2fAYd9r+ju1wLXFjO+Chh41NWJSIk+WraFSdNT2bh7P1ecmMiNZ/ekvvreSgT0KhGpZLbvOcDkt9N4c1EW3Vs1YOp1JzG4U9NYlyWViIJfpJJwd95clMXkt9PI2Z/H78/szm9P76q+t1JqCn6RSiArO5dJ01P5KH0rAzs24cGxA+jZpmGsy5JKSsEvUoEVFjr/99VaHnh3GYUOt53XhytPSlTfWzkmCn6RCipjyx5uTk5hwdqdnNI91Pe2YzP1vZVjp+AXqWDyCgp55pOVPPZBBnVrVefhnw1k7PHtdSKWRI2CX6QCScnM5qapKSzblMO5/dty5/l9admwdqzLkipGwS9SAeQeLOCRuek899lqWjaszZTLBjOyr/reStlQ8IvE2OcZob6363bs45IhCUwc3YtGddT3VsqOgl8kRnbty+Mvs9L414JMEpvX4/XxwxjWpXmsy5I4oOAXiYF3Uzdy+8wl7Nh7kOtO68oNZ3WnTk2diCXlQ8EvUo627N7PbW8uZvaSzfRt14gXrjyBfu3V91bKl4JfpBy4O298s56/zFrKwfxCJozqxbWndFbfW4kJBb9IGVuzbS8Tp6Xy5artDO3cjPvHDqBzi/qxLkvimIJfpIzkFxTy/OereWTucmpWq8a9F/Vn3Akd1fdWYk7BL1IG0rJ2MyE5hdQNuzird2vuubAfbRrXiXVZIgAccQejmXU0s4/MbKmZLTGz3xczx8zsMTPLMLMUMzs+bNkVZrYiuFwR7R9ApCLZn1fAQ7OXcf7jn7FxVy6P/2IQ/7h8sEJfKpRItvjzgT+5+7dm1hBYaGZz3T0tbM45QPfgMhR4ChhqZs2AO4AkwIP7znT3nVH9KUTK2IzvNvDQ7HSysnNp16QuN57dkwsHtf/BnG+Cvrertu7l4uPbc9u56nsrFVMkrRc3AhuD6zlmthRoD4QH/wXAy+7uwHwza2JmbYERwFx33wFgZnOBUcBrUf0pRMrQjO82MHFaKrl5BQBsyM5l4rRUAC4c1J6c/Xk8+F46/5y/lvZN6vLS1UM4rUfLWJYsclil2sdvZonAIOCrIovaA+vDbmcGYyWNi1QaD81O/0/oH5KbV8BDs9NpVLcGk6YvZtPu/Vw1PJE/j1TfW6n4In6FmlkDIBm4wd13F11czF38MOPFPf54YDxAQkJCpGWJlLms7Nxixzdk53L1iwvo0boBT1x6EscnqO+tVA4RnT1iZjUJhf4r7j6tmCmZQMew2x2ArMOM/4i7T3H3JHdPatlSb5Ol4mjXpG6Jy244qztv/79TFPpSqURyVI8BzwFL3f2REqbNBC4Pju4ZBuwKPhuYDYw0s6Zm1hQYGYyJVBo3nt2TukW+R8cMJozqyQ1n9aBWDZ19K5VLJLt6hgOXAalmtigYuwVIAHD3p4FZwGggA9gHXBUs22FmdwPfBPebfOiDXpHK4vyB7Zi3fCvTv9uAA43r1uSO8/pw8eAOsS5N5KhEclTPZxS/rz58jgO/K2HZ88DzR1WdSIyt2JzDhOQUvl2Xrb63UmXo8AORYhzML+TpT1by+IcZ1KtdnUd+PpCLBqnvrVQNCn6RIhatz2bC1BTSN+dw3oBQ39sWDdT3VqoOBb9IYN/BfP46ZzkvfL6aVg3r8OzlSZzVp3WsyxKJOgW/CPDZim1MnJ7C+h25XDo0gQnnqO+tVF0Kfolru/blcc87afx7YSZdWtTnjfHDGKq+t1LFKfglLrk77y7exO1vLmHnvoP8dkRX/vdM9b2V+KDgl7izefd+bpuxmDlpm+nXvhEvXX0Cfdup763EDwW/xA135/Vv1nNv0Pd24jm9uObkztRQ31uJMwp+iQtrtu3l5mkpzF+1g2FdmnH/xQNIVN9biVMKfqnS8gsKefaz1Tw6dzm1qlfjvotDfW91IpbEMwW/VFlLsnYxITmFxRt2M7JPa+6+sB+tG6kFooiCX6qc/XkFPPbBCp6Zt4qm9Wry5KXHc06/NtrKFwko+KVK+WrVdiZOS2XVtr38dHAHbj23N03qqe+tSDgFv1QJOfvzuP/dZbzy1To6NK3LP68Zwind1dBHpDgKfqn03k/bzK0zFrMlZz/XnNyZP43sQb1aemmLlET/O6TS2rbnAHfOXMLbKRvp2bohT182mOM6Nol1WSIVnoJfKh13Z9q3G7j7nTT2HSjgjz/pwXWndVULRJEIHTH4zex54Dxgi7v3K2b5jcClYY/XG2gZtF1cA+QABUC+uydFq3CJT+t37GPSjMXMW76VwZ2acv/F/eneumGsyxKpVCLZ4n8ReBx4ubiF7v4Q8BCAmY0B/lCkr+7p7r7tGOuUOFdQ6Lz0xRoenpOOAXed35fLhnWiWjUdoilSWpH03J1nZokRPt4lwGvHUpBIUSs253BTcgrfrctmRM+W/OWi/rRvUjfWZYlUWlHbx29m9YBRwPVhww7MMTMHnnH3KdF6Pqn6DuYX8uTHGTzxUQYNatfg0f8ZyIXHqe+tyLGK5oe7Y4DPi+zmGe7uWWbWCphrZsvcfV5xdzaz8cB4gISEhCiWJZXRd+t2MiE5heWb93D+wHbcMaYPzdX3ViQqohn84yiym8fds4J/t5jZdGAIUGzwB+8GpgAkJSV5FOuSSmTfwXwenr2cF75YTZtGdXj+yiTO6KW+tyLRFJXgN7PGwGnAL8PG6gPV3D0nuD4SmByN55Oqad7yrdwyPZXMnblcNqwTN43qSUP1vRWJukgO53wNGAG0MLNM4A6gJoC7Px1MuwiY4+57w+7aGpge7I+tAbzq7u9Fr3SpKrL3HeTut5eS/G0mXVrW59/XncgJic1iXZZIlRXJUT2XRDDnRUKHfYaPrQIGHm1hUvW5O++kbuTOmUvI3pfH9ad34/ozuqnvrUgZ05m7EhObdu3n1hmLeX/pZgZ0aMzLVw+lT7tGsS5LJC4o+KVcFRY6r32zjvtnLSOvsJBJo3tz1fBE9b0VKUcKfik3q7buYeK0VL5avYOTujbnvov706m5+t6KlDcFv5S5vIJC/vHpKv72/gpq16jGA2P78/Mk9b0ViRUFv5SpxRtCfW+XZO1mVN82TL6gL63U91YkphT8Uib25xXwt/dX8I9PV9Gsfi2euvR4zunfNtZliQgKfikD84O+t6u37eXnSR2YNLoPjevpRCyRikLBL1Gze38e981axmtfryOhWT1euXYow7u1iHVZIlKEgl+iYm7aZm6dkcrWnAP86pTO/PEnPalbSydiiVRECn45JltzDnDnW0t4J2Ujvdo0ZMplSQxU31uRCk3BL0fF3Un+dgN3v51G7sEC/jyyB78+rSs1dSKWSIWn4JdSW79jH7dMT+XTFdtI6tSU+8cOoFurBrEuS0QipOCXiBUUOi9+sYaHZ6dTzeDuC/py6VD1vRWpbBT8EpH0TTlMSE5h0fpsTu/ZknvU91ak0lLwy2EdyC/giY9W8tTHGTSsU5O/jzuO8we209ctiFRiCn4p0cK1O7k5OYUVW/Zw4XHtuH1MX5rVrxXrskTkGCn45Uf2HsjnodnpvPTlGto2qsMLV53A6T1bxbosEYmSIx57Z2bPm9kWM1tcwvIRZrbLzBYFl9vDlo0ys3QzyzCzm6NZuJSNT5ZvZeSj83jpyzVcPqwTc/54mkJfpIqJZIv/ReBx4OXDzPnU3c8LHzCz6sATwE+ATOAbM5vp7mlHWauUoZ17D3L322lM+24DXVvWZ+p1JzK4k/reilRFkfTcnWdmiUfx2EOAjKD3Lmb2OnABoOCvQNydt1I2ctfMJezKzeN/z+jG787oRu0a+roFkaoqWvv4TzSz74Es4M/uvgRoD6wPm5MJDC3pAcxsPDAeICEhIUplyeFs3JXLrdMX88GyLQzs0JhXfjWUXm3U91akqotG8H8LdHL3PWY2GpgBdAeKO97PS3oQd58CTAFISkoqcZ4cu8JC55Wv1/HAu8vILyzk1nN7c9XwzlTXiVgiceGYg9/dd4ddn2VmT5pZC0Jb+B3DpnYg9I5AYmjl1j1MTE7l6zU7OLlbC+69qD8JzevFuiwRKUfHHPxm1gbY7O5uZkMIHSm0HcgGuptZZ2ADMA74xbE+nxydvIJCpsxbxd8/WEGdGtV48KcD+NngDjoRSyQOHTH4zew1YATQwswygTuAmgDu/jTwU+A3ZpYP5ALj3N2BfDO7HpgNVAeeD/b9SzlLzdzFTckpLN24m9H923Dn+X1p1VB9b0XilYUyumJJSkryBQsWxLqMSi/3YAF/e385z362mub1azH5gn6M6tcm1mWJSBkws4XunhTJXJ25W0V9sXIbE6elsnb7Psad0JGJo3vTuK763oqIgr/K2ZWbx/3vLuW1r9fTqXk9Xv3VUE7qqr63IvJfCv4qZPaSTdw2YzHb9hzg16d24YazeqjvrYj8iIK/CtiSs587Zy5hVuomerdtxHNXnED/Do1jXZaIVFAK/krM3fn3wkz+8s5ScvMKuPHsnow/tYv63orIYSn4K6l120N9bz/L2MaQxGbcN7Y/XVuq762IHJmCv5IpKHRe+Hw1f52znOrVjHsu7McvhiSo762IREzBX4ks27SbCVNT+D5zF2f2asU9F/WjbWP1vRWR0lHwVwIH8gt4/MMMnvp4JY3r1uSxSwYxZkBbfd2CiBwVBX8Ft3DtDiYkp5KxZQ8XD2rPbef1oan63orIMVDwV1B7DuTz0HvLeHn+Wto1rsuLV53ACLVAFJEoUPBXQB+lb2HStFQ27t7PFScmcuPZPalfW78qEYkOpUkFsmPvQSa/tYQZi7Lo3qoBU687icGdmsa6LBGpYhT8FYC7M/P7LO56K42c/Xn8/szu/Pb0rup7KyJlQsEfY1nZudw6YzEfLtvCcR2b8MDYAfRs0zDWZYlIFabgj5HCQueVr9bywHvpFBQ6t53XhytPSlTfWxEpc5F04HoeOA/Y4u79ill+KTAhuLkH+I27fx8sWwPkAAVAfqRNAqq6jC17mDgthW/W7OSU7qG+tx2bqe+tiJSPSLb4XwQeB14uYflq4DR332lm5wBTgKFhy093923HVGUVkVdQyDOfrOSxDzKoW6s6D/9sIGOPb68TsUSkXB0x+N19npklHmb5F2E35wMdjr2sqiclM5ubpqawbFMO5w5oy51j+tKyYe1YlyUicSja+/ivAd4Nu+3AHDNz4Bl3nxLl56vwcg8W8MjcdJ77bDUtG9ZmymWDGdlXfW9FJHaiFvxmdjqh4D85bHi4u2eZWStgrpktc/d5Jdx/PDAeICEhIVplxdQXGdu4eVoq63bs45IhCUwc3YtGddT3VkRiKyrBb2YDgGeBc9x9+6Fxd88K/t1iZtOBIUCxwR+8G5gCkJSU5NGoK1Z27cvj3llLeWPBehKb1+P18cMY1qV5rMsSEQGiEPxmlgBMAy5z9+Vh4/WBau6eE1wfCUw+1uer6N5bvJHb3lzCjr0Hue60rtxwVnfq1NSJWCJScURyOOdrwAighZllAncANQHc/WngdqA58GRwdMqhwzZbA9ODsRrAq+7+Xhn8DBXClt37uf3NJby3ZBN92zXihStPoF979b0VkYonkqN6LjnC8muBa4sZXwUMPPrSKgd3518L1vOXd5ZyIL+QCaN6ce0pndX3VkQqLJ25ewzWbt/LxGmpfLFyO0M7N+P+sQPo3KJ+rMsSETksBf9RyC8o5PnPV/PI3OXUrFaNey/qz7gTOqrvrYhUCgr+UkrL2s3N01JIydzFWb1bc8+F/WjTuE6syxIRiZiCP0L780J9b5/+ZCVN6tXk8V8M4tz+6nsrIpWPgj8C36zZwc3JKazcupexx3fg1nN7q++tiFRaCv7DyNmfx4PvpfPP+Wvp0LQuL189hFN7tIx1WSIOaHqRAAAIyklEQVQix0TBX4IPl21m0vTFbNq9n6uHd+ZPI3uo762IVAlKsiK27znA5LfTeHNRFj1aN+DJS09iUIL63opI1aHgD7g7by7K4q63lrDnQD5/OKsHvxnRlVo1dCKWiFQtCn5gQ3Yuk6an8nH6VgYlhPre9mitvrciUjXFdfAXFjr/nL+WB99bhgN3jOnD5Seq762IVG1xG/wZW3KYkJzKwrU7ObVHS+69qB8dmqrvrYhUfXEX/AfzC3n6k5U8/mEG9WpX55GfD+SiQep7KyLxI66Cf9H6bG5ODvW9HTOwHXeM6UOLBup7KyLxJS6Cf9/BfP46ZzkvfL6aVg3r8OzlSZzVp3WsyxIRiYkqH/yfrdjGxOkprN+Ry6VDE5hwjvreikh8q7LBv2tfHve8k8a/F2bSpUV93hg/jKHqeysiQkRnJ5nZ82a2xcwWl7DczOwxM8swsxQzOz5s2RVmtiK4XBGtwkvi7sxK3ciZj3zCtO828NsRXZn1+1MU+iIigUi3+F8EHgdeLmH5OUD34DIUeAoYambNCPXoTQIcWGhmM91957EUXZIZ321g0vRU9h4soGZ14w9ndef6M7qXxVOJiFRaEW3xu/s8YMdhplwAvOwh84EmZtYWOBuY6+47grCfC4w61qKLM+O7DUycFgp9gLwC54mPVjLjuw1l8XQiIpVWtL6Ipj2wPux2ZjBW0njUPTQ7ndy8gh+M5eYV8NDs9LJ4OhGRSitawV/c2U9+mPEfP4DZeDNbYGYLtm7dWuoCsrJzSzUuIhKvohX8mUDHsNsdgKzDjP+Iu09x9yR3T2rZsvTNTto1qVuqcRGReBWt4J8JXB4c3TMM2OXuG4HZwEgza2pmTYGRwVjU3Xh2T+rWrP6Dsbo1q3Pj2T3L4ulERCqtiI7qMbPXgBFACzPLJHSkTk0Ad38amAWMBjKAfcBVwbIdZnY38E3wUJPd/XAfEh+1CweFPjp4aHY6Wdm5tGtSlxvP7vmfcRERCTH3Yne5x1RSUpIvWLAg1mWIiFQaZrbQ3ZMimav2UiIicUbBLyISZxT8IiJxRsEvIhJnFPwiInFGwS8iEmcq5OGcZrYVWHsMD9EC2BalcqJJdUWuItYEqqs0KmJNUHXr6uTuEX3tQYUM/mNlZgsiPZ61PKmuyFXEmkB1lUZFrAlUF2hXj4hI3FHwi4jEmaoa/FNiXUAJVFfkKmJNoLpKoyLWBKqrau7jFxGRklXVLX4RESlBpQt+MxtlZulmlmFmNxezvLaZvREs/8rMEsOWTQzG083s7HKs6Y9mlmZmKWb2gZl1CltWYGaLgsvMaNUUYV1XmtnWsOe/NmzZFWa2IrhcUc51PRpW03Izyw5bVibry8yeN7MtZra4hOVmZo8FNaeY2fFhy8pyXR2prkuDelLM7AszGxi2bI2ZpQbrKmpfdxtBTSPMbFfY7+n2sGWH/d2XcV03htW0OHgtNQuWldW66mhmH5nZUjNbYma/L2ZO+b+23L3SXIDqwEqgC1AL+B7oU2TOb4Gng+vjgDeC632C+bWBzsHjVC+nmk4H6gXXf3OopuD2nhiuqyuBx4u5bzNgVfBv0+B60/Kqq8j8/wc8Xw7r61TgeGBxCctHA+8Saic6DPiqrNdVhHWddOj5gHMO1RXcXgO0iMG6GgG8fay/+2jXVWTuGODDclhXbYHjg+sNgeXF/D8s99dWZdviHwJkuPsqdz8IvA5cUGTOBcBLwfWpwJlmZsH46+5+wN1XE2oaM6Q8anL3j9x9X3BzPqEWlGUtknVVkrOBue6+w913AnOBUTGq6xLgtSg9d4ncfR5wuCZBFwAve8h8oImZtaVs19UR63L3L4LnhXJ6bUWwrkpyLK/JaNdVXq+rje7+bXA9B1gKFO0OVe6vrcoW/O2B9WG3M/nxSvzPHHfPB3YBzSO8b1nVFO4aQn/dD6ljoSbz883swijUU9q6xgZvL6ea2aH+yGW1rkr12MEusc7Ah2HDZbW+jqSkustyXZVW0deWA3PMbKGZjS/nWk40s+/N7F0z6xuMVYh1ZWb1CAVocthwma8rC+12HgR8VWRRub+2Imq9WIFYMWNFD0sqaU4k9z0aET+umf0SSAJOCxtOcPcsM+sCfGhmqe6+spzqegt4zd0PmNl1hN4pnRHhfcuyrkPGAVPdvSBsrKzW15GU9+uqVMzsdELBf3LY8PBgXbUC5prZsmCruKx9S+jrA/aY2WhgBtCdCrKuCO3m+dx/2Aa2TNeVmTUg9IfmBnffXXRxMXcp09dWZdvizwQ6ht3uAGSVNMfMagCNCb39i+S+ZVUTZnYWMAk4390PHBp396zg31XAx4S2CKLhiHW5+/awWv4BDI70vmVZV5hxFHk7Xobr60hKqrss11VEzGwA8CxwgbtvPzQetq62ANOJzq7NI3L33e6+J7g+C6hpZi2oAOsqcLjXVdTXlZnVJBT6r7j7tGKmlP9rK9ofZpTlhdA7lFWE3v4f+nCob5E5v+OHH+7+K7jelx9+uLuK6Hy4G0lNgwh9qNW9yHhToHZwvQWwgih92BVhXW3Drl8EzPf/fqi0OqivaXC9WXnVFczrSegDNyuP9RU8ZiIlf2B5Lj/8AO7rsl5XEdaVQOjzqpOKjNcHGoZd/wIYVU41tTn0eyMUoOuC9RbR776s6gqWH9oQrF8e6yr4uV8G/naYOeX+2oraCi+vC6FPwJcTCtJJwdhkQlvSAHWAfwf/Gb4GuoTdd1Jwv3TgnHKs6X1gM7AouMwMxk8CUoP/AKnANeW8ru4DlgTP/xHQK+y+VwfrMAO4qjzrCm7fCdxf5H5ltr4IbQFuBPIIbWldA1wHXBcsN+CJoOZUIKmc1tWR6noW2Bn22loQjHcJ1tP3we94UjnWdH3Y62o+YX+Uivvdl1ddwZwrCR3kEX6/slxXJxPaPZMS9jsaHevXls7cFRGJM5VtH7+IiBwjBb+ISJxR8IuIxBkFv4hInFHwi4jEGQW/iEicUfCLiMQZBb+ISJz5/9cP3CF8HvgDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_pred = mx + b\n",
    "y_pred = lm.predict(x) \n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 = 0.9990766389658357\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print('R^2 =', r2_score(y_pred,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REGRESI NON-LINIER (POLINOM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "degree polinom (int)= 15\n",
      "RMSE = 1.4339396169230527\n",
      "R^2 = 0.9970642753009166\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 operator\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "np.random.seed(0)\n",
    "x = 2 - 3 * np.random.normal(0, 1, 20)\n",
    "y = x - 2 * (x ** 2) + 0.5 * (x ** 3) + np.random.normal(-3, 3, 20)\n",
    "\n",
    "# transforming the data to include another axis\n",
    "x = x[:, np.newaxis]\n",
    "y = y[:, np.newaxis]\n",
    "\n",
    "degr = int(input(\"degree polinom (int)= \"))\n",
    "polynomial_features= PolynomialFeatures(degree=degr)\n",
    "x_poly = polynomial_features.fit_transform(x)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x_poly, y)\n",
    "y_poly_pred = model.predict(x_poly)\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y,y_poly_pred))\n",
    "r2 = r2_score(y,y_poly_pred)\n",
    "print(\"RMSE =\", rmse)\n",
    "print(\"R^2 =\", r2)\n",
    "\n",
    "plt.scatter(x, y, s=10)\n",
    "# sort the values of x before line plot\n",
    "sort_axis = operator.itemgetter(0)\n",
    "sorted_zip = sorted(zip(x,y_poly_pred), key=sort_axis)\n",
    "x, y_poly_pred = zip(*sorted_zip)\n",
    "plt.plot(x, y_poly_pred, color='m')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}