bigdata/day7-2/设计任务.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:19:47.509049Z",
     "start_time": "2024-03-06T09:19:46.666579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Quality Grade  Color and lustre  Smoothness  Complexity  Weight  Length  \\\n",
      "0              1                 2           7        61.5    0.23    3.95   \n",
      "1              2                 2           6        59.8    0.21    3.89   \n",
      "2              4                 2           4        56.9    0.23    4.05   \n",
      "3              2                 6           5        62.4    0.29    4.20   \n",
      "4              4                 7           7        63.3    0.31    4.34   \n",
      "\n",
      "   Width  Thickness  Price  \n",
      "0   3.98       2.43    326  \n",
      "1   3.84       2.31    326  \n",
      "2   4.07       2.31    327  \n",
      "3   4.23       2.63    334  \n",
      "4   4.35       2.75    335  \n",
      "       Quality Grade  Color and lustre    Smoothness    Complexity  \\\n",
      "count   53940.000000      53940.000000  53940.000000  53940.000000   \n",
      "mean        2.095903          3.594197      4.948980     61.749405   \n",
      "std         1.116600          1.701105      1.647136      1.432621   \n",
      "min         1.000000          1.000000      1.000000     43.000000   \n",
      "25%         1.000000          2.000000      4.000000     61.000000   \n",
      "50%         2.000000          4.000000      5.000000     61.800000   \n",
      "75%         3.000000          5.000000      6.000000     62.500000   \n",
      "max         5.000000          7.000000      8.000000     79.000000   \n",
      "\n",
      "             Weight        Length         Width     Thickness         Price  \n",
      "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000  \n",
      "mean       0.797940      5.731157      5.734526      3.538734   3932.799722  \n",
      "std        0.474011      1.121761      1.142135      0.705699   3989.439738  \n",
      "min        0.200000      0.000000      0.000000      0.000000    326.000000  \n",
      "25%        0.400000      4.710000      4.720000      2.910000    950.000000  \n",
      "50%        0.700000      5.700000      5.710000      3.530000   2401.000000  \n",
      "75%        1.040000      6.540000      6.540000      4.040000   5324.250000  \n",
      "max        5.010000     10.740000     58.900000     31.800000  18823.000000  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.rcParams['font.sans-serif']=['SimHei']   \n",
    "matplotlib.rcParams['axes.unicode_minus']=False \n",
    "\n",
    "data = pd.read_csv(\"Gradeandpricepredictionofhandicrafts.csv\")\n",
    "print(data.head())\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "data.fillna(data.mean(), inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:19:47.515259Z",
     "start_time": "2024-03-06T09:19:47.509049Z"
    }
   },
   "id": "12cba69d3fdf8cfd",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Price'], bins=50)\n",
    "plt.xlabel('价格')\n",
    "plt.ylabel('频次')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:19:47.642561Z",
     "start_time": "2024-03-06T09:19:47.516805Z"
    }
   },
   "id": "9e533d74d4b7d7d0",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Quality Grade'].value_counts().sort_index(ascending=False).plot.bar()\n",
    "plt.xlabel('质量等级')\n",
    "plt.ylabel('数量')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:19:47.713724Z",
     "start_time": "2024-03-06T09:19:47.644010Z"
    }
   },
   "id": "8b42a29a44597f40",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['Complexity'])\n",
    "plt.xlabel('工艺复杂度')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:19:47.764659Z",
     "start_time": "2024-03-06T09:19:47.714724Z"
    }
   },
   "id": "7ee5bd73a091520d",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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RRIDsX1eaI7V46qQhFkIGqW5o1jWnmtiIIF1zqvEzmxDk79dtJsjfz2tX1gYA6aF58Y/A52nCkZDSHKnlwu7zl/oTN6F99pjRk4ZYCBmkp6sBl5tTzdHKel1zqtlTVoWG5u5v+zU0t3ntGDEA8Bf+bktzpJ464fs8aY7UcmH3+UuVuRq4+7xXC7fKbvlIc6rpqQi43JxqfH2MGAAEBnR/Rexyc6Qe3956mjwVCyGD1DbKlsuV5lTTJFwfR5pTTVW9bOyXNKcizSkrcqU5IlKLp261xELIIL4+PiLcKnuXL82pJiJIdqVPmlNRvfB2hzRHRGrh9Hkfl5YgG/wlzanm2LlGXXOqKTwh20xXmlOR9Fqfd14TJAAIFa4XKs2RWjh93sd9d6ZO15xqgv1lv2rSnGoOnpatmC3NEaloYKxsnSxpjtQSHRqoa04v3nnW8UDl1bIrHdKcamLCZetCSHOq0YSr40hzKgoUHpo0R+o5XNmga47U4hSO/ZHm9MJCyCD9bLL1caQ51TQ2t+qaU41NOPZJmlORv/B2hzRH6jkvnBUqzZFavhKO/ZHm9MJCyCC+vpDYaeEIWGlONdU97DN3uTkV2YIDdM2ReqRv9A2+IECG8cwFFFgIGaS8SnhrTJhTTUgPqypfbk41lcICT5pT0XnhPnLSHKmHA+Z926gB0brm9MJCyCA/ChfKk+ZUMyA6WNecapya7KVdmlNRbZOswJHmiEgt6UlRsAV3v0SILdgf6UlRBvWoHQshg0SHCUfLC3Oq6WOTFTjSnGqCA2QDX6Q5JXHXVSKf5mc24cW7UrvNvHhXKrfY8FYW4T+sNKcaX99rbFBsuK45FZmEt/2lOSJST8bgPlgxezhiwzu+6Y8ND8SK2cORMbiP4X3y4refnmVIPxuAE8Kc9/H1LTZCg2R/atKciqQ3vHhjjMi7ZQzug4kpsdhTVoUzdU3oHda+47zRV4Iu8N5XXQ9TI5wNJM2ppn9UCHYePifKeSPOliEC/CArdL1zygRdzM9swiiDxwJdCm+NGeSccDNNaU41E38Ro2tONd8cl22dIc0REZE+eEXIICUna3XNqabyvKzAk+ZUc75JtlCkNEekIt4epQuaW51Yl38Mx6sakBAZjN+M6o8Ai3uuzbAQMkigcA8taU41W/afFufuviX+GvfGeDwBtF9+lowA886/ACK6YPFHpVj5ZVmHoQAvfHQAvxudiLzJKYb3h685ZIiKWtlCkdKcaoKFgx6kORUFCo9NmiMi9Sz+qBSv/b2s03hIpwa89vcyLP6o1PA+sRAyiiYcBSvNKcYkXBxGmlONXbhasjSnIg4YJ/Jtza1OrPyyrNvMyi/L0Gzw7GEWQgb5sU62dYI0p5rQANmvmjSnmsYWWYEjzamoTVjgSHNEpJZ1+cd6fKPj1NpzRvLOs44HamqRTYuX5lTTLHybL82pplV4XNKcilqFhybNEZFajlc16JrTi9sKobNnzyIxMRHHjh1zPTdv3jyYTCbX44YbbnC1lZSUIC0tDb169UJubi60i24h7dixA4MGDUJ0dDSWLl3a4fu8++67SEhIQFxcHDZu3HjNj+tSwqyyHbWlOdVYLbKBH9KcatqEF3qkOSIi1SREyrZQkub04pZC6OzZs5g6dWqHIggA9u7di82bN6O6uhrV1dUoLCwEADgcDkybNg0jRozA3r17UVpairVr1wIAKisrkZmZiaysLOTn52P9+vXYvn07gPbiKTs7G/Pnz8cnn3yCBQsW4NChQ0YeqktkDxvNXW5ONYP7RuiaU430D42XaInIW/1mVH/0tHi02dSeM5JbXndnzZqF++67r8Nzra2t2L9/P8aMGQObzQabzYawsDAAwMcff4za2losXboUSUlJWLRoEVavXg0AWL9+PeLi4jB//nwkJydjwYIFrrZVq1Zh3LhxmDNnDlJTU/Hoo49i3bp1l+yXw+GA3W7v8NBLjC1I15xq6h2y9XGkOdUECOtbaY6ISDUBFjN+Nzqx28zvRicavp6QWwqhlStXYt68eR2eKy4uhtPpxLBhwxAUFISMjAycONG+N1dRURHS09MRHNx+uWzIkCEoLS11tY0bNw4mU3uZOXLkSBQUFLjaxo8f7/oeF7d1ZfHixYiIiHA94uP1W88mKTpU15xqztQ16ZpTTZhVtmSXNEdEpKK8ySl4eExipytDZhPw8BgfWkcoMbFzRVhaWoqBAwdi3bp12LdvHywWCx566CEAgN1u7/A5JpMJfn5+qK6u7tQWHh6OU6dOdfl5F7d1JS8vD7W1ta5HeXn5VR/rBXePkBVV0pxqGptl0yGlOdUECy/1SHNERKrKm5yCg8/diflTBuH+UQmYP2UQDj53p1uKIMCDVpbOzs5Gdna26+NXXnkFiYmJsNvtsFgsCAwM7JC3Wq1oaGjo1HbheQDdtnUlMDCw0/fRy39tPSjOPfevqdekD+4UFSI7wUtzqqkT3vKT5lTElaWJ6IIAixn/z+gB7u4GAA9+zenduzecTicqKioQGRmJysrKDu11dXUICAjo1HbheQDdthmt7KxsOqA0p5p+wlkA0pxq/HsaIXiZORVJl4jy0qWkiMhDecxLTm5uLjZs2OD6OD8/H2azGfHx8UhLS0N+fr6rraysDA6HA5GRkZ3aCgsL0bdvXwDots1oQcI9xKQ51YwaEK1rTjkmYYEjzSmIPwIi8kQec9YdOnQonn76aXz22WfYunUrcnJycP/99yM4OBhjxoyB3W7HmjVrAACLFi3ChAkT4Ofnh8zMTOzatQvbtm1DS0sLlixZgkmTJgEAZs6cibfeegvFxcWor6/Hyy+/7Goz2rhfXKdrTjVm4dlNmlNNuHADLWlORdLdQ7x4lxEi8kAeM0Zo9uzZ2L9/P2bOnAk/Pz/Mnj0bixYtAtA+1mfVqlXIyspCbm4uzGYzvvjiCwBAdHQ0li1bhsmTJyM0NBQ2m821xtDQoUPx+OOP45ZbboHVakVycjLmzp3rluPbfrCy59A/c/fd2v/adsYNfrTLZoNJc6ppEp7cpTkVSYfBe+dweSLyVG4thLSfbTC6ePFiLF68uMtsZmYmjhw5goKCAqSnpyMqKsrVlpOTg0mTJuHgwYMYPXo0QkN/moL+wgsvIDs7GydPnsTYsWPdNkbI1/eaKjheJc7dNaLfNe6N8XoFWXBMmPNWJgCS3TO885ogEXkqpV51Y2NjMWXKlC7bEhMTu5yWDwApKSlISXHPtLwL+keFYOfhc6KcNzp4WrY4pTSnmuujQlD4Q8/Hdr2X/vsDsiLocnJERHrwmDFC3u5XA3vrmlOPb28yMWVwnK45IiLSh3eedTxQQXmNrjnVJMfIrnRIc6r5W8EJXXNERKQPFkIG0YQX/KU51UQIN5OV5lRz6Md6XXNERKQPFkIGsQXJTvDSnGp+rHXomlONb98YJCLyXHzdNUh0qGzrDmlONbHhVl1zqnE6ZbMBpTkiItIHCyGDxEYE6ZpTTZ2jRdecajThpHBpjoiI9MFCyCAjEnqhp22kzKb2nHeSnuC9sxAI8petGC3NERGRPlgIGaTgeDWcPYyDdmrtOW/k22UQEBoo+1OT5oiISB981TVIRU2jrjnVBAfIrnRIc6ppaJXNBpTmVCT9l/XO3wAi8lQshAxSWC670iPNqab0lGzFaGlONVHBsq1dpDkVSYeBc7g4ERmJhZBBfH17gSDhlR5pTjXXhclmA0pzRESkDxZCBkkU7iElzanmlv6RuuZU4+uzBomIPBULIYP8ZlR/0ayx34zqb0h/jJYSG65rTjVHK2UrRktzRESkDxZCBgmwmPGrQd1vqPqrQb0RYPHOf5KqxmZdc6ppbHHqmiMiIn1451nXA7U5NZSc7H4gcMlJO9p6mmOvqN5hshWjpTnV+Pd0OfAyc0REpA8WQgbZU1aFitqmbjMVtU3YU1ZlUI+Mldo3QtecakpO1uiaIyIifbAQMshpe/dF0OXmVLP4o1Jdc6qpbmrVNUdERPpgIWSQqnrZrurSnGr+ceScrjnVaMI7ntIcERHpg4WQQSKC/HXNqaauSbjpqjCnmuhg2Z+aNKci6e4h3GWEiIzElxyDSPcQ89a9xqR7iXrrnqMDrpMtCyDNqchq0TdHROpqc2rIP3IOm749ifwj59w6UYgvOQYpOC4bBC3NqSYmzIoKe89Xe2K8dNbY0IRe2H2sRpTzVk3CvTOkOSJS05aSCjzzQWmHMbGx4VY8k5mCjMF9DO8PrwgZpLlNVu1Kc6SWCKvw1qgwpyLpwgBcQIDIe20pqUDOm990mhh02t6EnDe/wZaSCsP7xELIIAOiZFsnSHOqqW6Qjf2R5lSz8/uzuuZUFOAnK3GkOSJSS5tTw3+8V9xtJu+9YsNvk7EQMkhooOwupDSnmvMtsvsd0pxqTtbIlkWQ5lTUIrzaKc0RkVp2Hz2Hmh7e7FY3tGD3UWNnD7MQMkhDq+zFXZpTTd8I2dgfaU41tcKtQ6Q5JZmEV3qkOSJSSr5weRRpTi8shAzS1ibbQ0qaU01USICuOdWYICtwpTkVtQqv9EhzRKQa6d82b415pfKqBl1zqqk6Lxv7I82pxtdvjQJAi/C1TZojIrWMGhCta04vLIQMUtcsG/sizanmh5pGXXOqqWmQ3fKS5oiIVJOeFAVbcPczY23B/khPijKoR+1YCBnE6ZQVONKcaqSbqnvr5uvS+tZL62AiIviZTXjxrtRuMy/elQo/g08ELISM4pm3Rg2TdF2orjnVSKeDunN1VSKiay1jcB+smD0cseGBHZ6PDQ/EitnD3bKgovcOSPAwtpBAVJ7veWdxW0hgjxkVzfnlAOSXFYhy3kg6/pfjhInI22UM7oPxv4jBuvxjOF7VgITIYPxmVH8EWNxzbYaFkEGmD4vDf239XpTzRt+frRfnfnWN++IOPn5BkIjIpastNlZ+WcYtNrydWbg2ijSnmm0HftQ1R0RE6uEWGxc5e/YsEhMTcezYMddzmzZtwoABA2CxWDBs2DAcOHDA1TZv3jyYTCbX44YbbnC1lZSUIC0tDb169UJubi407af31Tt27MCgQYMQHR2NpUuXGnJsXfn84Bldc6qxN8qmxUtzqvHTOUdEpBpusXGRs2fPYurUqR2KoCNHjuDBBx/Eiy++iJMnT+LGG2/EnDlzXO179+7F5s2bUV1djerqahQWFgIAHA4Hpk2bhhEjRmDv3r0oLS3F2rVrAQCVlZXIzMxEVlYW8vPzsX79emzfvt3IQ3WpFZ7gpTnVWISzAKQ51QQJ91KV5oiIVMMtNi4ya9Ys3HfffR2eO3DgAF588UXcc889iImJwSOPPOIqdlpbW7F//36MGTMGNpsNNpsNYWFhAICPP/4YtbW1WLp0KZKSkrBo0SKsXr0aALB+/XrExcVh/vz5SE5OxoIFC1xtRnMI99CS5lQT6C/7VZPmVBNgkV3rkeaIiFTDLTYusnLlSsybN6/Dc1OnTsVDDz3k+vjQoUNITk4GABQXF8PpdGLYsGEICgpCRkYGTpw4AQAoKipCeno6goODAQBDhgxBaWmpq23cuHEw/XPczciRI1FQcOmZSw6HA3a7vcOD9BHkLxuXL82p5uLbtXrkiIjU45nTRtxSCCUmJnbb3tzcjL/85S/IyckBAJSWlmLgwIFYt24d9u3bB4vF4iqa7HZ7h69nMpng5+eH6urqTm3h4eE4derUJb/v4sWLERER4XrEx8dfzWF2YBIOgpbmlCM9wXtpIeDUZP+u0hwRkWq4xcZlWLhwIUJCQlxjhLKzs7F3716MGjUKycnJeOWVV/Dpp5/CbrfDYrEgMLDj2jtWqxUNDQ2d2i48fyl5eXmora11PcrLy3U7pqToEF1zqgkKkN3ykeZU42gV3hoV5oiIVJOWGIme3uqZ/pkzksfdh/j888+xfPly7N69G/7+XY8c7d27N5xOJyoqKhAZGYmSkpIO7XV1dQgICEBkZCQqKys7PX8pgYGBnYoqvcREWHXNqabqvGwPLWlONW1OfXNERKopOF7d400v7Z+5UQbuN+ZRV4TKysqQlZWF5cuXIyUlxfV8bm4uNmzY4Po4Pz8fZrMZ8fHxSEtLQ35+foev4XA4EBkZ2amtsLAQffv2NeZgfua8o+dVpS8np5qz9bICR5pTTavwjp80R0SkmjN1TT2HLiOnF48phBobGzF16lRMnz4dM2bMQH19Perr66FpGoYOHYqnn34an332GbZu3YqcnBzcf//9CA4OxpgxY2C327FmzRoAwKJFizBhwgT4+fkhMzMTu3btwrZt29DS0oIlS5Zg0qRJbjm+U7UOXXOqOSe80iPNqcYzhwgSERmnd5jsjoc0pxePuTW2detWlJaWorS0FCtXrnQ9X1ZWhtmzZ2P//v2YOXMm/Pz8MHv2bCxatAgAYLFYsGrVKmRlZSE3NxdmsxlffPEFACA6OhrLli3D5MmTERoaCpvN5lpjyGh9bVbsPS7LeSOT8BQvzZF6TJAVehwuTuSdRiZGok+EFadrm7p8LTABiI2wYqQvjRG6eKrw9OnTu506vHjxYixevLjLtszMTBw5cgQFBQVIT09HVNRP9xZzcnIwadIkHDx4EKNHj0ZoqHt2N7/r5n7YVNTz0uF33dzPgN4YLzjQgvMtPS8WGRzoMbU56YxXxYh8m5/ZhIXTUvDIm990emN04Q3Qwmkp8DN4YV2PuTV2tWJjYzFlypQORdAFiYmJuPPOO91WBAGAxU/2o5bmVNNXOAhcmiMiIvVkDO6DV2cPR+zPXutjI6x4dfZwt2y6yrffBjlTLxv7I82p5vqoUHx7sk6UIyIi75UxuA8mpsRiT1kVztQ1oXdY++0wo68EXcBCyCBn62QFjjSnmqAA2ZUuaY6IiNTlZzYZOkW+OyyEDHJOeKVHmlPNd6frdc0REZG62pwarwj5mpKTtbrmVFNefekVva8kR0REatpSUoFnPijFaftP6wXFhlvxTGaKW8YI8T6EQaz+sq0jpDnVSOt8Tp0mIvJeW0oqkPPmNx2KIAA4bW9CzpvfYEtJz7Or9cZCyCAxEbKtO6Q51ThahHttCXNERKSWNqeG/3ivuNtM3nvFaHP6wO7zvmhoP5uuOdXYm2WbaElzqrn0DndXllOR9Fqnd14TJaLdR8+hpqH79eSqG1qw++g5g3rUjoWQQYqFY3+kOdX4+q0x6cYh3rnBSDvptT5eEyTyTv84clbXnF5YCBnE0Sq70iHNERERqeRkdaOuOb2wEDLIzu9lFa40pxrp9EROYyQi8k5xwr00pTm9sBAyyLn6pp5Dl5FTjVN4z0uaIyIitfwy6Tpdc3phIWSQZuHAB2lONSbhJABpjoiI1JKeFAVbsH+3GVuwP9INXnGahZBBfH3GTFiQ7FdNmiMiIrX4mU2495Z+3WbuvaUfd5/3VvGRsnue0pxqkqJDdM0REZFa2pwaPijqfsHED4oquI6Qt7outPvLgZebU83Rs7JZANIcERGpZU9ZFSpqux8HW1HbhD1lVQb1qB0LIYMcr5atECPNqaaqsVXXHBERqeXn22pcbU4vLIQMYm+UFTjSHBERkUqq6h265vTCQsggVots8Jc0R0REpJLIENkmQtKcXlgIGaRFOPhLmiMiIlJJbESQrjm9sBAyiHQUvNGj5YmIiIwwMjGyx3WEegX7Y2RipEE9asdCyCAhVtlsMGmOiIhINc097KfZU/u1wELIILPTr9c1R0REpJLdR8+hoYftE843t2H30XMG9agdCyGDBFpk24lKc0RERCrJPyIrcKQ5vbAQMsg3x6t1zREREalEg2wMrDSnFxZCBulpNc3LzREREakkXDgGVprTCwshgzS1tOiaIyIiUkmNcMFgaU4vLIQMI10okQsqEhGR96moEd4ZEeb0wkLIINIpge6YOkhERHSttTpl5zdpTi8shIzCC0JEROTDyirP65rTCwshgwT7y6bFS3OqkR6Vdx49ERF5qqsuhJqbm6FpGpwGX8pSzXmHbPCXNKcaP+FvmjRHRERq6RNh1TWnlys67dTV1eGhhx5CTEwMgoODUVxcjH79+qGgoEDv/nmN2qbuV9O83JxqWAgREfm2STfF6prTyxWddh588EEcO3YMf/3rXxESEoKIiAg88cQT+P3vf693/7yGv1k2+EeaU43JLPtVk+aIiEgt/SJDdM3p5YrOOtu2bcPq1auRkZEBs9kMk8mEBx54APv379e7f17jutAAXXOqsVn9dM0REZFavGr3+V/84hdYu3YtAMBkMsFkMuHvf/87brrpJvHXOHv2LBITE3Hs2DHXcyUlJUhLS0OvXr2Qm5sLTftpme0dO3Zg0KBBiI6OxtKlSzt8rXfffRcJCQmIi4vDxo0bO7QtX74cMTExGDBgAD7//PPLP1idBPjLTvDSnGpahCumS3NEROR93HEKuKJC6H/+53/w8ssvo2/fvqirq8M999yDxx9/HMuXLxd9/tmzZzF16tQORZDD4cC0adMwYsQI7N27F6Wlpa5iq7KyEpmZmcjKykJ+fj7Wr1+P7du3A2gvnrKzszF//nx88sknWLBgAQ4dOgQA+OSTT/CHP/wBr7/+Ot58803MmTMH584Zu5nbBSfONeiaU43mFO4xI8wREZFa9pRVoaah+90TahpasKesyqAetbuiQigtLQ2HDx/GkiVL8MILL+Cxxx5DaWkpRowYIfr8WbNm4b777uvw3Mcff4za2losXboUSUlJWLRoEVavXg0AWL9+PeLi4jB//nwkJydjwYIFrrZVq1Zh3LhxmDNnDlJTU/Hoo49i3bp1AIBXX30VDzzwAKZPn47bbrsN06dPx//+7/9esl8OhwN2u73DQy/1DtkgaGlONQEW4RUxYY6IiNRy2i5bMVqa08sVj0yNiIhAdnY2/vjHP2LChAmIiIgQf+7KlSsxb968Ds8VFRUhPT0dwcHBAIAhQ4agtLTU1TZu3DiYTO0DiUeOHOmaoVZUVITx48e7vo60rSuLFy9GRESE6xEfHy8+pp5IFxfw1kUIwgJlBY40R0REaqkUFjjSnF6uqBAqLS3F8OHD8c477wAAfvWrX+Gmm27Cd999J/r8xMTETs/Z7fYOz5tMJvj5+aG6urpTW3h4OE6dOtXl50nbupKXl4fa2lrXo7y8XHQ8EtK9dI3dc9c49qZWXXNERKSW/adqdc3p5YoKoYcffhhjx47FHXfcAQDYvXs3pk2bhpycnCvuiMViQWBgYIfnrFYrGhoaOrVdeL6rz5O2dSUwMBDh4eEdHnqRLhjtpQtL+/ytQSIiX/dDTaOuOb1c0Wn322+/xdtvv+26HRYSEoLHHnsMKSkpV9yRyMhIlJSUdHiurq4OAQEBiIyMRGVlZafnL3zelbQZLTI0EPXVDlHOG9mFBY40R0REagkUrpgrzenlir5bamqqa0bXBW+++eZlTZ//ubS0NOTn57s+Lisrg8PhQGRkZKe2wsJC9O3bt8vPk7YZ7dYB0brmiIiIVHLxkjh65PRyRYXQ8uXL8dJLLyElJQVTpkzBTTfdhP/+7//Gq6++esUdGTNmDOx2O9asWQMAWLRoESZMmAA/Pz9kZmZi165d2LZtG1paWrBkyRJMmjQJADBz5ky89dZbKC4uRn19PV5++WVX2913341XXnkFJ0+exI8//ojVq1e72own/Yfl9HEiIvI+zW2y85s0p5crujV2880347vvvsPmzZtRXl6O2bNnY8qUKVc1psZisWDVqlXIyspCbm4uzGYzvvjiCwBAdHQ0li1bhsmTJyM0NBQ2m811RWro0KF4/PHHccstt8BqtSI5ORlz584FAEybNg3vvPMOkpOTAbQP6r7rrruuuI9XY+/xGl1zREREKunXKwgFJ2pEOSNd8dDc8PBwZGVlXdU3//nlr8zMTBw5cgQFBQVIT09HVFSUqy0nJweTJk3CwYMHMXr0aISGhrraXnjhBWRnZ+PkyZMYO3asaxyQyWTCunXrMG/ePJw/fx5jx451TcE3WkurbGK8NEdERKSSu4fHY1NRhShnJI+boxQbG4spU6Z02ZaYmNjl1HsASElJueRg7bS0NN36d6VCA2R3IaU5IiIildyWHI3gAD80NF96UkxwgB9uSzZ2rCzPugbhOjpEROTL/MwmLL1naLeZpfcMhZ/Z2Ds34kLIz8/PteWE2WyGn59fh8eF56hrrcKxX9KcaoKEvxrSHBERqSdjcB+smD0cMWEdl7KJCQvAitnDkTG4j+F9Et8aO3r0qGswdFlZ2TXrkLeKDgnAmbpmUc4bhQRa0NjQ89WukECPu1tLREQ6yhjcBxNTYrGnrApn6prQO8yKkYmRhl8JukB81klISOjy/0nmlzdGo/R0vSjnjRzCQeDSHBERqavNqaH0VC2OVzUgITIYIxJ6eX4hRFcnOsSqa041dc2yAkeaIyIiNS3+qBQrvyyD86KhIC98dAC/G52IvMlXvkPFlWIhZJCaxp5vi11OjoiISDWLPyrFa3/vPLzGqcH1vNHF0BXNGnvttddQUdHzWgD0k1PVTbrmVCO94OmeC6NERHStNbc68fqX3Y8xfv3LMjQbPETiigqhZcuWobCwUO++eLXTtZfe9f5KcqoJEc4Gk+aIiEgtf/3HMfS0jZimteeMdEWF0Pz58/H888+jvr7nwb/UrrTCrmtONfXCTeWlOSIiUsvXx6p0zenlisYIHT58GABwww034IEHHkBISIirbcGCBfr0zMucd8gu9UlzREREKgkOkF3yl+b0ckWF0LFjxzBw4EAMHDgQZ86ccT3vrn28VMAxMkRE5Mtm3twP7397SpQz0mUVQqdPn8aOHTswfvx4/PKXv8SAAQOuVb+IiIjIi9yaFNVz6DJyehGPEdq6dSuSk5PxwgsvYNmyZUhNTcXatWuvYdfImwQIL3VJc0REpJaC49W65vQiLoT++Mc/YtmyZdi3bx+++eYbvP/++3jiiSeuZd+8ilW4q7w0pxo/4WFJc0REpJaKmkZdc3oRn3ZKS0sxbdo018cTJ05EU1MT1xMSmjjoOl1zqpEuC8EdNoiIvFNhuexKjzSnF3Eh1NbWhuDg4A7PBQUFobW15400CSirPK9rTjUtPawdcbk5IiJSi/Tl3ejTgHiwtKZpuP766zvMDKupqUFqairM5p/qqaoqY+f/q+JolexSnzRHRESkksSokJ5Dl5HTi7gQ2r59+7Xsh9cL9DOjDj2vFhjopYNkzAAkd7288+iJiOjetOvx3OYDopyRxIXQ2LFjr2U/vN5NsSHYcaRGlPNG0qE/HCJEROSdNnx1XJx7aGzSNe7NT/gG3CB1zbJTvDRHRESkkq2lp3XN6YWFkFFMwh+1NEdERKQQe5NscpU0pxeedQ1is8r2TpHmiIiIVPKLmFBdc3phIWSQmsYWXXNEREQq+UWfcF1zemEhZBhuu0pERL7ru9N1uub0wkLIIJomWyJKmiMiIlJJfXPPS8hcTk4vLIQMEhQgW6lAmlONdOQTR0gREXknzSmbFS3N6YWFkEGCA2SneGlONSbhHT9pjoiI1FLd0KxrTi8shAwS7C/7UUtzqvEX1nfSHBERqYW3xnxco3BZBGlONaGB/rrmiIhILVHBAbrm9MJCyCCRIbITvDSnmgA/2SBwaY6IiNQSHRqoa04vLIQMcuJcva451dQ5ZAWONEdERGqpOi8b+yPN6YWFkEHKzjXqmlONBuHyAcIcERGpxVNnT7MQMoifcDaUNKcaP2GBI80REZFaYiJkt7ykOb14VCG0du1amEymTo+1a9ciMzOzw3MTJkxwfd6OHTswaNAgREdHY+nSpR2+5rvvvouEhATExcVh48aNRh+SS7hVVuFKc6ppES4LIc0REZFahsf30jWnF48qhO677z5UV1e7HuXl5YiOjsbo0aOxd+9eFBcXu9o2bdoEAKisrERmZiaysrKQn5+P9evXY/v27QCAkpISZGdnY/78+fjkk0+wYMECHDp0yC3H1rdXsK451QQHygo8aY6IiNTSO9yqa04vHlUIBQQEwGazuR5vvPEGZsyYAavVCk3TMHjwYFdbSEgIAGD9+vWIi4vD/PnzkZycjAULFmD16tUAgFWrVmHcuHGYM2cOUlNT8eijj2LdunVuObZbB0TpmlPN4D5huuaIiEgtB0/bdc3pxaMKoYs1NTXhpZdewp/+9Cfs2bMHbW1t6NevH0JCQjBr1ixUV1cDAIqKijBu3DiY/rkk8ciRI1FQUOBqGz9+vOtrXtzWFYfDAbvd3uGhlwduS9Q1p5oQ4fpA0hwREanl2Nnzuub04rGF0IYNG3Drrbeif//+OHjwIIYOHYrNmzdj9+7dKCsrQ15eHgDAbrcjMfGn4iE8PBynTp3qsa0rixcvRkREhOsRHx9/jY7O93xXKVsWQJojIiK1HBTuKi/N6cVjC6EVK1YgJycHAJCXl4dPP/0UQ4cORWpqKv785z/j3XffBQBYLBYEBv40wtxqtaKhoaHHtq7k5eWhtrbW9SgvL9fteP76j2O65lSjCSeDSXNERKSWQIus5JDm9OKRhdDhw4dx+PBhTJw4scv23r1749y5c3A4HIiMjERlZaWrra6uDgEB7ctzd9fWlcDAQISHh3d46GXPsXO65lTzixjZ2B9pjoiI1GIVbiYpzenFIwuht99+G1OnToW/f/t4kXvvvRc7d+50tefn5yMmJgaBgYFIS0tDfn6+q62wsBB9+/YFgG7bjNbgkG0iJ82pxhog/AMQ5oiISC31TbIVo6U5vXhkIbRlyxbcfvvtro9TU1PxxBNPYOfOnXj//feRl5eHRx55BACQmZmJXbt2Ydu2bWhpacGSJUswadIkAMDMmTPx1ltvobi4GPX19Xj55ZddbUZzOmUL5Ehzqvn2RI2uOSIiUsuB07IxoNKcXjxu0ZbGxkZ89dVXeP31113PPfXUUygrK0NGRgbCwsIwd+5c/OlPfwIAREdHY9myZZg8eTJCQ0Nhs9mwdu1aAMDQoUPx+OOP45ZbboHVakVycjLmzp3rjsNCc5ts8Is0pxp7U6uuOSIiUktrm+yNvjSnF48rhIKCguBwODo85+/vj9WrV7vWB/q5nJwcTJo0CQcPHsTo0aMRGhrqanvhhReQnZ2NkydPYuzYsd2OEbqW+vUKQoHgake/XkHXvjNu0NQiK3CkOSIiUoyHzprxuELoSiUmJnaYKn+xlJQUpKSkGNyjju4eHo9NRRWinDeS3oP1yHu1RER01ZzC+kaa0wvPOwa5NUm4srQwpxpNuJmsNEdERIoxC1/gpTmdsBAyyNfHqnTNqcYi/L2W5oiISC0RgbJZwdKcXlgIGWTX92d1zakmMEB2F1aaIyIitTiFY3+kOb2wEDLIvh9qdM2ppq9NtpuwNEdERGqxN8nWyZPm9MJCyCCBwns+0pxq6oXT4qU5IiJSS6twVrw0pxcWQgapamjRNaeac+dlxyXNERGRWqQbBxi9wQALIYM4WmWX+qQ51bQKV8yW5oiISC3RIf665vTCQsggZpPsRy3NqcYWJBsELc0REZFaWoVjoKU5vXjnWdcDJUbJVoyW5lQTHSYbBC3NERGRWqobZGNApTm9sBAySHxUiK451dQ1yn6xpTkiIlKLQ7iXpjSnFxZCBvll0nW65lRjMslmw0lzREREemAhZJDhCb10zakmwCL7VZPmVCMt71gGEhEZyzvPOh5ow1fHdc2pJjJYNgtAmlMNCyEiIs/EQsggx6sadM2pJtQaoGtONSyEiIg8EwshgyREBuuaU43VX3aKl+ZUI10fzOB1xIiIfB4LIYP8ZlR/mHs4x5tN7Tlv9P2P9brmVBMQICvwpDkiItVICw6jCxMWQgYJsJjxu9GJ3WZ+NzrRawcLn6l36JpTTahwzXhpjohINdJJ8Qavp8hCyEh5k1MwpF94l21D+oUjb3KKwT0yjtVPeGtMmFOOdMVwL11ZnIiIhRBh8Uel2PeDvcu2fT/YsfijUoN7ZJwAi+xKhzSnmnqHbDNZaY6IiPTBQsggza1OrPyyrNvMyi/L0NzqnZuOxkbIts6Q5lRT3yx7jyPNERGRPlgIGWRd/jE4ezjHObX2nDcyC1eMluZUw+nzROTrPPV1kIWQQY5WymZDSXOqiQ4L1DWnmiCLvjkiItVcHyF7gZPm9MJCyCAHT9fpmlPNufpmXXOq6SWscKQ5IiLVWK2yN7rSnF5YCBkkUDgtXppTzXXhsl9saU41TW2yi73SHBGRairtTbrm9OKdZ10PFBIoe6cvzakmMSpU15xq7I2y2WDSHBGRauxNbbrm9MJCyCB3pMTomlPNjb1lBY40R0REamkVToqV5vTCQsggcb1ke4hJc6rZc7xK15xqWoR/2NIcEZFqPHXPRRZCRvHUJTUNcrK6UdccERGppU9EgK45vbAQMoiv77UVZ5MtlCjNqcZTNxskIjJKnHDBXGlOL3zdNUiVsMCR5lRza/8oXXOqka4X7p3rihMRASdrZec3aU4vLIQMEhkiu9QnzanmwOmu91i70pxqPPXeOBGRUVp62l7hMnN6YSFkkNiIIF1zqtl24Eddc6oJEFY40hwRkWqswnXypDm9sBAyyMjESPTp4b5nnwgrRiZGGtQjY0kLfIPfCBhGEx6XNEdEpBqzcL1YaU4vHlcIzZs3DyaTyfW44YYbAAAlJSVIS0tDr169kJubC+2iM8aOHTswaNAgREdHY+nSpR2+3rvvvouEhATExcVh48aNhh7LxfzMJiycltJtZuG0FPgZ/RtgEJtVdqlDmlNNs3DwjzRHRKSaM3bhpCFhTi8eVwjt3bsXmzdvRnV1Naqrq1FYWAiHw4Fp06ZhxIgR2Lt3L0pLS7F27VoAQGVlJTIzM5GVlYX8/HysX78e27dvB9BePGVnZ2P+/Pn45JNPsGDBAhw6dMiNR+e7zp6X7SEmzRERkVo8dRUZjyqEWltbsX//fowZMwY2mw02mw1hYWH4+OOPUVtbi6VLlyIpKQmLFi3C6tWrAQDr169HXFwc5s+fj+TkZCxYsMDVtmrVKowbNw5z5sxBamoqHn30Uaxbt84tx9bm1PDsh6XdZp79sBRtXnpv6Psz53XNqSZQeKFLmiMiUk2TcMloaU4vHlUIFRcXw+l0YtiwYQgKCkJGRgZOnDiBoqIipKenIzi4fdXlIUOGoLS0vagoKirCuHHjYDK131IaOXIkCgoKXG3jx493ff2L27ricDhgt9s7PPSyp6wKFbXdbyRXUduEPWXeubJyY4vsno80p5owq2wPOWmOiEg1vCIkUFpaioEDB2LdunXYt28fLBYLHnroIdjtdiQmJrpyJpMJfn5+qK6u7tQWHh6OU6dOAUC3bV1ZvHgxIiIiXI/4+Hjdju20cDddaY7UYm9q1TVHRET68KhCKDs7G3v37sWoUaOQnJyMV155BZ9++imcTicCAwM7ZK1WKxoaGmCxWDq0XXgeQLdtXcnLy0Ntba3rUV5ertuxna2TDf6S5kgtzcLNlKU5IiLVBAkrDmlOLx59Hb53795wOp2IjY1FSUlJh7a6ujoEBAQgMjISlZWVnZ4H0G1bVwIDAzsVXHo5J1wxWpojtXBlaSLydf2vC8GBH3seB9r/uhADevMTj7oilJubiw0bNrg+zs/Ph9lsRmpqKvLz813Pl5WVweFwIDIyEmlpaR3aCgsL0bdvXwDots1oxSdrdc2RWvx1zhERqWbUDdG65vTiUYXQ0KFD8fTTT+Ozzz7D1q1bkZOTg/vvvx933HEH7HY71qxZAwBYtGgRJkyYAD8/P2RmZmLXrl3Ytm0bWlpasGTJEkyaNAkAMHPmTLz11lsoLi5GfX09Xn75ZVeb0RpbZGM/pDlSi9Vftj6UNEdEpJqjlbJZwdKcXjzq1tjs2bOxf/9+zJw5E35+fpg9ezYWLVoEi8WCVatWISsrC7m5uTCbzfjiiy8AANHR0Vi2bBkmT56M0NBQ2Gw21xpDQ4cOxeOPP45bbrkFVqsVycnJmDt3rluOLUC4ZLg0pxo/AJLhL946e/x8i2wehDRHRKSab8trdM3pxaMKIaB95tbixYs7PZ+ZmYkjR46goKAA6enpiIr6aZfynJwcTJo0CQcPHsTo0aMRGhrqanvhhReQnZ2NkydPYuzYsd2OEbqW7A0tuuZUYzYDbYIBMGbvrAM5RoiIfN55h+yOhzSnF48rhLoTGxuLKVOmdNmWmJjYYar8xVJSUpCS0v32FtfahXWO9MqpJshiQktzz1c7gizeefxERL7OYgYkS8UZfWPES99/e57YHjZcvdycalraZLd8pDkiIlJLcICs5JDm9MJCyCADesumA0pzqmkSro8jzRERkVpMwpJDmtMLCyGDVNplm4lKc6rx1KXViYjIGOeFK8ZKc3phIWSQ2HDhrTFhjoiISCUO4Waq0pxeWAgZpKpBdqVHmiMiIlKJdCqM0VNmWAgZZOf3lT2HLiNHRESkkkDhPHVpTi8shAxSWSccIyTMERERqcTfLLvWI83phYWQQZxO2T1PaY6IiEgltYK15C4npxcWQgYJC5RVuNIcERERXT0WQgYJEd70lOaIiIjo6rEQMkh9s2wXKWmOiIiIrh4LIYO0Csf+SHNERER09VgIGcRf+JOW5oiIiOjq8bRrkJY22S0vaY6IiIiuHgshgzS06JsjIiKiq8dCyCDcdJSIiMjzsBAiIiIin8VCiIiIiHwWCyEiIiLyWSyEiIiIyGexECIiIiKfxUKIiIiIfBYLISIiIvJZLISIiIjIZ7EQIiIiIp/FQoiIiIh8Fgshg0h/0PwHISIiMg7PuwYJseibIyIioqvHQsggTW365oiIiOjqsRAySItwW3lpjoiIiK4eCyEiIiLyWSyEiIiIyGexECIiIiKfxUKIiIiIfJbHFUKbNm3CgAEDYLFYMGzYMBw4cAAAMG/ePJhMJtfjhhtucH1OSUkJ0tLS0KtXL+Tm5kLTfhpxvGPHDgwaNAjR0dFYunSp4cdDREREnsujCqEjR47gwQcfxIsvvoiTJ0/ixhtvxJw5cwAAe/fuxebNm1FdXY3q6moUFhYCABwOB6ZNm4YRI0Zg7969KC0txdq1awEAlZWVyMzMRFZWFvLz87F+/Xps377dXYdHREREHsajCqEDBw7gxRdfxD333IOYmBg88sgjKCwsRGtrK/bv348xY8bAZrPBZrMhLCwMAPDxxx+jtrYWS5cuRVJSEhYtWoTVq1cDANavX4+4uDjMnz8fycnJWLBggautKw6HA3a7vcODiIiIvJdHFUJTp07FQw895Pr40KFDSE5ORnFxMZxOJ4YNG4agoCBkZGTgxIkTAICioiKkp6cjODgYADBkyBCUlpa62saNGweTyQQAGDlyJAoKCi75/RcvXoyIiAjXIz4+/lodKhEREXkAjyqELtbc3Iy//OUvyMnJQWlpKQYOHIh169Zh3759sFgsroLJbrcjMTHR9Xkmkwl+fn6orq7u1BYeHo5Tp05d8nvm5eWhtrbW9SgvL792B0hERERu57E7Wy1cuBAhISGYM2cO/P39kZ2d7Wp75ZVXkJiYCLvdDovFgsDAwA6fa7Va0dDQ0KntwvOXEhgY2OlrERERkffyyELo888/x/Lly7F79274+/t3au/duzecTicqKioQGRmJkpKSDu11dXUICAhAZGQkKisrOz1PREREBHjgrbGysjJkZWVh+fLlSElJAQDk5uZiw4YNrkx+fj7MZjPi4+ORlpaG/Pz8Dp/vcDgQGRnZqa2wsBB9+/Y17mCIiIjIo3lUIdTY2IipU6di+vTpmDFjBurr61FfX48hQ4bg6aefxmeffYatW7ciJycH999/P4KDgzFmzBjY7XasWbMGALBo0SJMmDABfn5+yMzMxK5du7Bt2za0tLRgyZIlmDRpkpuPkoiIiDyFR90a27p1K0pLS1FaWoqVK1e6ni8rK8O9996LmTNnws/PD7Nnz8aiRYsAABaLBatWrUJWVhZyc3NhNpvxxRdfAACio6OxbNkyTJ48GaGhobDZbK41hoiIiIg8qhCaPn16h1WhL7Z48WIsXry4y7bMzEwcOXIEBQUFSE9PR1RUlKstJycHkyZNwsGDBzF69GiEhoZek74TERGRejyqELoasbGxmDJlSpdtiYmJHabRExEREQEeNkaIiIiIyEgshIiIiMhnsRAiIiIin8VCiIiIiHwWCyEiIiLyWSyEiIiIyGexECIiIiKfxUKIiIiIfBYLISIiIrrmpCs4G73SMwshIiIiuubuSInWNacXFkJERER0zQ2Oj9Q1pxcWQkRERHTNlZys1TWnFxZCREREdM19+f1ZXXN6YSFERERE11ydo03XnF5YCBEREZHPYiFEREREPouFEBEREfksFkJERETks1gIERERkc9iIUREREQ+i4UQERER+SwWQkREROSzWAgRERGRz2IhRERERD6LhRARERH5LBZCRERE5LNYCBEREZHPYiFEREREPouFEBEREfksFkJERETks1gIGWTWrXG65oiIiFQSEWjSNacXFkIGmZYar2tONbcmhemaU02Ev745IiLV/Etyb11zemEhZJD0AVGwBXd/lusV7I/0AVEG9chYj44ZpGtONUvvGaFrTkUxOudUc53OORX56ZwjtcxKS9A1pxefKIRKSkqQlpaGXr16ITc3F5qmGd4HP7MJL96V2m1m8V2p8DMbe0nQKLclRyM4oPuXt+AAP9yWHG1Qj4x1+00x6Omf1mxqz3mrL/4zQ9ecanY9f6euORUVPTNJ15xqihbcoWtONZ56HvD6QsjhcGDatGkYMWIE9u7di9LSUqxdu9YtfckY3AcrZg9HbLi1w/N9IqxYMXs4Mgb3cUu/jOBnNmHpPUO7zSy9Z6jXFoJ+ZhNeyR7ebeaV7OFee/wAEBTgh4kp3V/ynpjSG0E9vFCqKsBixsNjErvNPDwmEQEW731ZDrVaMKRfeLeZIf3CEWq1GNQjY0UE+yMhKqjbTEJUECJ6uHugKk89D5g0d1weMdD777+P3/72t/jhhx8QHByMoqIi/P73v8fOnTt7/Fy73Y6IiAjU1tYiPLz7P97L0ebUsKesCmfqmtA7zIqRiZFefQK82JaSCizcVIIf65pdz8WEBeDZ6YO9uhC8YEtJBZ5+rwhnG9pcz0UH++H5u4b6xPEDwO/e+Bqflp7p9PzElN5YeX+aG3pkrMUfleK1v5d1ev7hMYnIm5zihh4ZL/P/fol9P9g7PT+kXzg+eHS0G3pkrLF//hzHzzV2ej4hKgg7cse7oUfGMuI8cDnnb68vhJ599ll89dVX+OijjwAAmqYhKioKVVVVnbIOhwMOh8P1sd1uR3x8vO6FkK/z5UIQ4PEDQGNzGxZ9VIpj5xrQPyoYf5qc4rVXgrrS3OrEuvxjOF7VgITIYPxmVH+vvhLUlfqmVjzxt0KcqG7E9b2CsOzem732SlBXahta8Nu1e3CqtglxEVb8v/820muvBHXlWr8OshC6yJNPPommpiYsX77c9dx1112H7777Dr169eqQfeaZZ/Dss892+hoshIiIiNRxOYWQ178FsVgsCAwM7PCc1WpFQ0NDp2xeXh5qa2tdj/LycqO6SURERG7g9dchIyMjUVJS0uG5uro6BAQEdMoGBgZ2KpqIiIjIe3n9FaG0tDTk5+e7Pi4rK4PD4UBkZKQbe0VERESewOsLoTFjxsBut2PNmjUAgEWLFmHChAnw8/OdgZlERETUNa+/NWaxWLBq1SpkZWUhNzcXZrMZX3zxhbu7RURERB7A6wshAMjMzMSRI0dQUFCA9PR0REV55zYWREREdHl8ohACgNjYWEyZMsXd3SAiIiIP4vVjhIiIiIguhYUQERER+SyfuTV2JS4sum23d94Th4iIiDzThfO2ZPMMFkLdqKurAwDEx8e7uSdERER0uerq6hAREdFtxuv3GrsaTqcTp06dQlhYGEwmfTfFvLCha3l5uU/uY8bj9+3jB/gz8PXjB/gz4PFfu+PXNA11dXWIi4uD2dz9KCBeEeqG2WxGv379run3CA8P98k/gAt4/L59/AB/Br5+/AB/Bjz+a3P8PV0JuoCDpYmIiMhnsRAiIiIin8VCyE0CAwOxcOFCn93tnsfv28cP8Gfg68cP8GfA4/eM4+dgaSIiIvJZvCJEREREPouFEBEREfksFkJEblBTU4OvvvoK1dXV7u4KEZFPYyHkBmfPnkViYiKOHTvm7q64xaZNmzBgwABYLBYMGzYMBw4ccHeXDPXOO++gf//+mDNnDvr164d33nnH3V1yq4yMDKxdu9bd3TDUvHnzYDKZXI8bbrjB3V1ym6eeegrTpk1zdzcMtXbt2g7//hcevvR3sGrVKsTHxyM4OBi33347jh496r7OaGSoyspK7dZbb9UAaGVlZe7ujuEOHz6s9erVS/vb3/6mnT59Wvv1r3+t3Xbbbe7ulmFqamq06OhoraioSNM0TVuzZo2WkJDg3k650ZtvvqkB0NasWePurhhq1KhR2ubNm7Xq6mqturpas9vt7u6SWxQVFWmhoaHakSNH3N0VQzkcDte/fXV1tVZeXq5FR0drhw8fdnfXDHH48GEtPj5eKygo0I4fP6799re/1UaPHu22/vCKkMFmzZqF++67z93dcJsDBw7gxRdfxD333IOYmBg88sgjKCwsdHe3DGO32/Hf//3fGDJkCABg+PDhOHfunJt75R5VVVV48sknMXDgQHd3xVCtra3Yv38/xowZA5vNBpvNhrCwMHd3y3BOpxMPPfQQnnjiCQwYMMDd3TFUQECA69/eZrPhjTfewIwZM5CUlOTurhmisLAQ6enpGD58OK6//nr89re/xeHDh93WHxZCBlu5ciXmzZvn7m64zdSpU/HQQw+5Pj506BCSk5Pd2CNjxcfHIzs7GwDQ0tKCZcuWYcaMGW7ulXs8+eSTmDFjBtLT093dFUMVFxfD6XRi2LBhCAoKQkZGBk6cOOHubhluxYoVKC4uRv/+/fHBBx+gubnZ3V1yi6amJrz00kv405/+5O6uGCYlJQWff/45vv32W9TW1uKVV17BxIkT3dYfFkIGS0xMdHcXPEZzczP+8pe/ICcnx91dMVxRURFiY2OxZcsWvPzyy+7ujuG2b9+Ozz77DEuWLHF3VwxXWlqKgQMHYt26ddi3bx8sFkuHNwe+oL6+HgsXLsSAAQNw/PhxLFu2DP/yL/+CxsZGd3fNcBs2bMCtt96K/v37u7srhklJScHdd9+Nm2++GTabDfn5+fiv//ovt/WHhRC5zcKFCxESEoI5c+a4uyuGGzJkCLZu3Yrk5GSfO/6mpiY8/PDDePXVV33yllB2djb27t2LUaNGITk5Ga+88go+/fRT2O12d3fNMO+99x7Onz+P7du349lnn8Wnn36Kuro6rFu3zt1dM9yKFSt87s3gnj178OGHH2L37t2oqalBVlYWJk+eDM1N6zuzECK3+Pzzz7F8+XJs2LAB/v7+7u6O4UwmE0aMGIG//vWveO+991BTU+PuLhnmueeeQ1paGqZMmeLurniE3r17w+l0oqKiwt1dMcwPP/yA9PR0REdHAwAsFguGDBni1nEi7nD48GEcPnzYrbeF3GHjxo2YNWsWbr31VkREROD555/HkSNHUFRU5Jb+sBAiw5WVlSErKwvLly9HSkqKu7tjqB07diA3N9f1cUBAAEwmE8xm3/lT3LBhAzZt2uQaKLphwwbMnTsXc+fOdXfXDJGbm4sNGza4Ps7Pz4fZbEZ8fLwbe2Wsfv36dboNdvz4cfTt29dNPXKPt99+G1OnTvW5N4NOpxNnzpxxfVxXV4eGhga0tbW5pT8Wt3xX8lmNjY2YOnUqpk+fjhkzZqC+vh4AEBISApPJ5ObeXXs33ngjXn/9dSQnJ+POO+/E008/jTvuuAPh4eHu7pphvvzyS7S2tro+/sMf/oD09HT827/9m/s6ZaChQ4fi6aefRkxMDNra2vDYY4/h/vvvR3BwsLu7ZpgpU6bgsccew4oVKzB16lS89957KCoq8rk1tbZs2eIzv/cXGz16NB544AEMHz4cMTExWLVqFWJjY12zaQ3nton7Pg4+uo7Q+++/rwHo9PCln8XWrVu1lJQULSwsTLv77ru1M2fOuLtLbvXAAw/43DpC//Ef/6FFRERokZGR2rx587T6+np3d8lwO3fu1NLT07WgoCBtwIAB2gcffODuLhmqoaFBCwgI0A4cOODurhjO6XRq//mf/6ldf/31mr+/v3bzzTdr33zzjdv6w93niYiIyGf5zsAEIiIiop9hIUREREQ+i4UQERER+SwWQkREROSzWAgRERGRz2IhRERERD6LhRARERH5LBZCROQWu3fvxogRIxAWFoYJEybg5MmThvdh7dq1uP322w3/vkTkOVgIEZHhGhoaMH36dDz66KMoLS1FWFgYHnvssWv6Pfv3748vvvjimn4PIlIPCyEiMtyBAwdQU1ODBx98EPHx8ViwYIHbNlwkIt/GQoiIDBcfHw+z2Yznn38era2tuPnmm7Fp0yb0798fOTk5iIiIwJNPPok777wTUVFR+PrrrwEAf//73zFs2DD06tUL9913H2pqalxf81JtGRkZMJlMOH78OMaNGweTyYQXX3yxQ39WrlyJmJgYxMTE4L333gPw022zrtqA9g0zU1NTYbPZMGfOHDgcDlfbsmXLEBcXh7CwMNx7771oamoCAGiahj/+8Y+47rrr0KtXLzz66KPgLkdEbua2Xc6IyKe98847WkREhJaQkKC98cYbmqZpWkJCgpaVlaW99tprGgDt/fff18aPH689//zz2okTJ7SQkBDt9ddf144cOaJNmzZNmz59uqZpWrdt9fX1WnV1tRYfH699+OGHWnV1tdbU1KRpmqatWbNGi4qK0jIyMrTvv/9ee+qpp7T4+Pge277//nstICBAW7lypfbdd99pqamp2nPPPadpmqYdOHBA8/Pz03bs2KF999132vDhw7UVK1ZomqZpH3/8sWaz2bRvv/1W27dvn9avXz9ty5YtRv3IiagLLISIyG1qamq0hQsXalarVfvDH/6gJSQkaB9//LG2fft2LSYmRtO09t3pFy5cqC1atEibOHGi63N/+OEHDYBWUVHRbdsFCQkJ2vbt2zt8/zVr1mhWq1X78ccfNU3TtEOHDmkX3h921/b8889rI0eOdH2dV199VUtLS9M0TdOOHj2q+fn5aZ9++qnW0tLiemiapn3xxReazWbTvv76a62trU1rbm7WWltbdflZEtGVsbj1chQR+aRTp06hsbERSUlJeOaZZ3D77bdj/PjxAACr1drhvxeUl5djwIABro/79u2LwMBAnDhxotu22NjYbvsyaNAg9O7dGwAQEBAgavvhhx9QWFgIm80GAGhtbUVoaCgAIDExEa+99hry8vJw+PBhZGRk4OWXX8Z1112HsWPH4o9//CMefPBBVFRU4Ne//jX+8pe/IDg4WPyzIyJ9cYwQERnub3/7G+bMmeP6eMyYMbBYLK7CoivXX389jh496vr41KlTcDgcSEhI6LbtArPZ3OV4nPDw8Et+z0u19evXD9OmTcO3336Lb7/9FkVFRfj0009d3/uWW27B119/jWPHjuHs2bN47rnnAABHjx7FXXfdheLiYpSUlGDXrl1YsWLFJb8/EV17LISIyHATJkzAP/7xD2zcuBEnT57EM888gz59+nS6CnSx7Oxs/OMf/8DKlStRVlaGRx55BP/6r/+KmJiYbtsuSEpKwtatW1FRUYHPPvvsqvo/a9YsfPnll/j+++8RGBiI//mf/8GDDz4IACgpKcEdd9yBXbt2oa6uDkD7FSMA2LZtG2bMmIHCwkI0NjZ2aCMi92AhRESGS01NxZo1a7Bw4UIMHDgQ27dvx6ZNmzrdmrpYfHw8Nm/ejOXLl+Pmm29GcHAw1qxZ02PbBX/+85+xefNmXH/99XjmmWeuqv9JSUl444038O///u+44YYbsG/fPmzcuBEAcMcdd+Dhhx/Gr3/9a9x4443QNA1PP/00AODBBx/E2LFjMWnSJAwZMgTJycmYO3fuVfWFiK6OSevqWjERERGRD+AVISIiIvJZLISIiIjIZ7EQIiIiIp/FQoiIiIh8FgshIiIi8lkshIiIiMhnsRAiIiIin8VCiIiIiHwWCyEiIiLyWSyEiIiIyGf9/1q3RfJnSl3xAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_list = ['Quality Grade', 'Color and lustre', 'Smoothness', 'Complexity', 'Weight']\n",
    "for col in _list:\n",
    "    plt.scatter(data[col], data['Price'])\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Price')\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:19:48.517952Z",
     "start_time": "2024-03-06T09:19:47.764659Z"
    }
   },
   "id": "104d0dc4bc8b04ea",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression(max_iter=500, solver='liblinear')",
      "text/html": "<style>#sk-container-id-5 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-5 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-5 pre {\n  padding: 0;\n}\n\n#sk-container-id-5 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-5 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-5 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-5 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-5 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-5 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-5 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-5 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-5 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-5 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-5 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-5 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-5 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-5 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n#sk-container-id-5 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-5 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-5 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-5 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-5 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-5 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-5 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-5 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=500, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "le = LabelEncoder()\n",
    "data[\"Quality Grade\"] = le.fit_transform(data[\"Quality Grade\"])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop(\"Quality Grade\", axis=1), data[\"Quality Grade\"], test_size=0.25, random_state=2024)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "model = LogisticRegression(max_iter=500)\n",
    "model.fit(X_train_scaled, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:31:26.556683Z",
     "start_time": "2024-03-06T09:31:26.216832Z"
    }
   },
   "id": "e17710987c4427e2",
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.48      0.84      0.61      5405\n",
      "           1       0.57      0.51      0.54      3410\n",
      "           2       0.44      0.13      0.20      3003\n",
      "           3       1.00      0.00      0.00      1251\n",
      "           4       0.92      0.47      0.63       416\n",
      "\n",
      "    accuracy                           0.51     13485\n",
      "   macro avg       0.68      0.39      0.40     13485\n",
      "weighted avg       0.56      0.51      0.45     13485\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "print(classification_report(y_test, y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:31:26.575730Z",
     "start_time": "2024-03-06T09:31:26.559135Z"
    }
   },
   "id": "183b0923964733f4",
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.82      0.61      5405\n",
      "           1       0.58      0.53      0.55      3410\n",
      "           2       0.45      0.15      0.23      3003\n",
      "           3       1.00      0.00      0.00      1251\n",
      "           4       0.92      0.47      0.62       416\n",
      "\n",
      "    accuracy                           0.51     13485\n",
      "   macro avg       0.69      0.40      0.40     13485\n",
      "weighted avg       0.56      0.51      0.46     13485\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"C\": [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "    \"penalty\": [\"l2\"],\n",
    "}\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "gscv = GridSearchCV(model, param_grid, cv=5)\n",
    "gscv.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = gscv.predict(X_test_scaled)\n",
    "print(classification_report(y_test, y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:34:25.574265Z",
     "start_time": "2024-03-06T09:34:19.935200Z"
    }
   },
   "id": "7f1f461f73aa6ad1",
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()",
      "text/html": "<style>#sk-container-id-6 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-6 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-6 pre {\n  padding: 0;\n}\n\n#sk-container-id-6 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-6 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-6 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-6 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-6 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-6 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-6 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-6 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-6 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-6 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-6 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-6 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-6 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-6 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-6 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-6 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n#sk-container-id-6 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-6 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-6 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-6 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-6 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-6 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-6 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-6 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-6 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-6 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X = data.drop('Price', axis=1)\n",
    "y = data['Price']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=2024)\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:39:30.570602Z",
     "start_time": "2024-03-06T09:39:30.555366Z"
    }
   },
   "id": "4dbef42a69018bef",
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 802.998434130017\n",
      "MSE: 1499616.5750225848\n",
      "RMSE: 1224.5883287956751\n",
      "MAPE: 0.4410460064763934\n",
      "R2: 0.9073168343584097\n",
      "Explained Variance: 0.9073543014656212\n",
      "Accuracy: 0.0\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[24], line 13\u001B[0m\n\u001B[0;32m     11\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mExplained Variance:\u001B[39m\u001B[38;5;124m'\u001B[39m, explained_variance_score(y_test, y_pred))\n\u001B[0;32m     12\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAccuracy:\u001B[39m\u001B[38;5;124m'\u001B[39m, accuracy_score(y_test, y_pred \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0.5\u001B[39m))\n\u001B[1;32m---> 13\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mF1 Score:\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[43mf1_score\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_test\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_pred\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m>\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0.5\u001B[39;49m\u001B[43m)\u001B[49m)\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001B[0m, in \u001B[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    207\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    208\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m config_context(\n\u001B[0;32m    209\u001B[0m         skip_parameter_validation\u001B[38;5;241m=\u001B[39m(\n\u001B[0;32m    210\u001B[0m             prefer_skip_nested_validation \u001B[38;5;129;01mor\u001B[39;00m global_skip_validation\n\u001B[0;32m    211\u001B[0m         )\n\u001B[0;32m    212\u001B[0m     ):\n\u001B[1;32m--> 213\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    214\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m InvalidParameterError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    215\u001B[0m     \u001B[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001B[39;00m\n\u001B[0;32m    216\u001B[0m     \u001B[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001B[39;00m\n\u001B[0;32m    217\u001B[0m     \u001B[38;5;66;03m# the name of the estimator by the name of the function in the error\u001B[39;00m\n\u001B[0;32m    218\u001B[0m     \u001B[38;5;66;03m# message to avoid confusion.\u001B[39;00m\n\u001B[0;32m    219\u001B[0m     msg \u001B[38;5;241m=\u001B[39m re\u001B[38;5;241m.\u001B[39msub(\n\u001B[0;32m    220\u001B[0m         \u001B[38;5;124mr\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mw+ must be\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    221\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__qualname__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m must be\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    222\u001B[0m         \u001B[38;5;28mstr\u001B[39m(e),\n\u001B[0;32m    223\u001B[0m     )\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1271\u001B[0m, in \u001B[0;36mf1_score\u001B[1;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001B[0m\n\u001B[0;32m   1091\u001B[0m \u001B[38;5;129m@validate_params\u001B[39m(\n\u001B[0;32m   1092\u001B[0m     {\n\u001B[0;32m   1093\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_true\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marray-like\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msparse matrix\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1118\u001B[0m     zero_division\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwarn\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m   1119\u001B[0m ):\n\u001B[0;32m   1120\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Compute the F1 score, also known as balanced F-score or F-measure.\u001B[39;00m\n\u001B[0;32m   1121\u001B[0m \n\u001B[0;32m   1122\u001B[0m \u001B[38;5;124;03m    The F1 score can be interpreted as a harmonic mean of the precision and\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1269\u001B[0m \u001B[38;5;124;03m    array([0.66666667, 1.        , 0.66666667])\u001B[39;00m\n\u001B[0;32m   1270\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1271\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfbeta_score\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1272\u001B[0m \u001B[43m        \u001B[49m\u001B[43my_true\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1273\u001B[0m \u001B[43m        \u001B[49m\u001B[43my_pred\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1274\u001B[0m \u001B[43m        \u001B[49m\u001B[43mbeta\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1275\u001B[0m \u001B[43m        \u001B[49m\u001B[43mlabels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlabels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1276\u001B[0m \u001B[43m        \u001B[49m\u001B[43mpos_label\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpos_label\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1277\u001B[0m \u001B[43m        \u001B[49m\u001B[43maverage\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maverage\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1278\u001B[0m \u001B[43m        \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1279\u001B[0m \u001B[43m        \u001B[49m\u001B[43mzero_division\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mzero_division\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1280\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:186\u001B[0m, in \u001B[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    184\u001B[0m global_skip_validation \u001B[38;5;241m=\u001B[39m get_config()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mskip_parameter_validation\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m    185\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m global_skip_validation:\n\u001B[1;32m--> 186\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    188\u001B[0m func_sig \u001B[38;5;241m=\u001B[39m signature(func)\n\u001B[0;32m    190\u001B[0m \u001B[38;5;66;03m# Map *args/**kwargs to the function signature\u001B[39;00m\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1463\u001B[0m, in \u001B[0;36mfbeta_score\u001B[1;34m(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\u001B[0m\n\u001B[0;32m   1283\u001B[0m \u001B[38;5;129m@validate_params\u001B[39m(\n\u001B[0;32m   1284\u001B[0m     {\n\u001B[0;32m   1285\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_true\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marray-like\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msparse matrix\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1312\u001B[0m     zero_division\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwarn\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m   1313\u001B[0m ):\n\u001B[0;32m   1314\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Compute the F-beta score.\u001B[39;00m\n\u001B[0;32m   1315\u001B[0m \n\u001B[0;32m   1316\u001B[0m \u001B[38;5;124;03m    The F-beta score is the weighted harmonic mean of precision and recall,\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1460\u001B[0m \u001B[38;5;124;03m    0.12...\u001B[39;00m\n\u001B[0;32m   1461\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1463\u001B[0m     _, _, f, _ \u001B[38;5;241m=\u001B[39m \u001B[43mprecision_recall_fscore_support\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1464\u001B[0m \u001B[43m        \u001B[49m\u001B[43my_true\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1465\u001B[0m \u001B[43m        \u001B[49m\u001B[43my_pred\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1466\u001B[0m \u001B[43m        \u001B[49m\u001B[43mbeta\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbeta\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1467\u001B[0m \u001B[43m        \u001B[49m\u001B[43mlabels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlabels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1468\u001B[0m \u001B[43m        \u001B[49m\u001B[43mpos_label\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpos_label\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1469\u001B[0m \u001B[43m        \u001B[49m\u001B[43maverage\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maverage\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1470\u001B[0m \u001B[43m        \u001B[49m\u001B[43mwarn_for\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mf-score\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1471\u001B[0m \u001B[43m        \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1472\u001B[0m \u001B[43m        \u001B[49m\u001B[43mzero_division\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mzero_division\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1473\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1474\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m f\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:186\u001B[0m, in \u001B[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    184\u001B[0m global_skip_validation \u001B[38;5;241m=\u001B[39m get_config()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mskip_parameter_validation\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m    185\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m global_skip_validation:\n\u001B[1;32m--> 186\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    188\u001B[0m func_sig \u001B[38;5;241m=\u001B[39m signature(func)\n\u001B[0;32m    190\u001B[0m \u001B[38;5;66;03m# Map *args/**kwargs to the function signature\u001B[39;00m\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1767\u001B[0m, in \u001B[0;36mprecision_recall_fscore_support\u001B[1;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001B[0m\n\u001B[0;32m   1604\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Compute precision, recall, F-measure and support for each class.\u001B[39;00m\n\u001B[0;32m   1605\u001B[0m \n\u001B[0;32m   1606\u001B[0m \u001B[38;5;124;03mThe precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1764\u001B[0m \u001B[38;5;124;03m array([2, 2, 2]))\u001B[39;00m\n\u001B[0;32m   1765\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m   1766\u001B[0m _check_zero_division(zero_division)\n\u001B[1;32m-> 1767\u001B[0m labels \u001B[38;5;241m=\u001B[39m \u001B[43m_check_set_wise_labels\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_true\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_pred\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maverage\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_label\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1769\u001B[0m \u001B[38;5;66;03m# Calculate tp_sum, pred_sum, true_sum ###\u001B[39;00m\n\u001B[0;32m   1770\u001B[0m samplewise \u001B[38;5;241m=\u001B[39m average \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msamples\u001B[39m\u001B[38;5;124m\"\u001B[39m\n",
      "File \u001B[1;32m~\\scoop\\apps\\python\\current\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1556\u001B[0m, in \u001B[0;36m_check_set_wise_labels\u001B[1;34m(y_true, y_pred, average, labels, pos_label)\u001B[0m\n\u001B[0;32m   1554\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m   1555\u001B[0m             average_options\u001B[38;5;241m.\u001B[39mremove(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msamples\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m-> 1556\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   1557\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTarget is \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m but average=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m. Please \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1558\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mchoose another average setting, one of \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m (y_type, average_options)\n\u001B[0;32m   1559\u001B[0m         )\n\u001B[0;32m   1560\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m pos_label \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m1\u001B[39m):\n\u001B[0;32m   1561\u001B[0m     warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m   1562\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNote that pos_label (set to \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m) is ignored when \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1563\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124maverage != \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m (got \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m). You may use \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1566\u001B[0m         \u001B[38;5;167;01mUserWarning\u001B[39;00m,\n\u001B[0;32m   1567\u001B[0m     )\n",
      "\u001B[1;31mValueError\u001B[0m: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, mean_absolute_percentage_error, explained_variance_score, accuracy_score, f1_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print('MAE:', mean_absolute_error(y_test, y_pred))\n",
    "print('MSE:', mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('MAPE:', mean_absolute_percentage_error(y_test, y_pred))\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('Explained Variance:', explained_variance_score(y_test, y_pred))\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred >= 0.5))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T09:40:10.607951Z",
     "start_time": "2024-03-06T09:40:10.377554Z"
    }
   },
   "id": "66c2aa439eb755cb",
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "c6b5a8df8372f03"
  }
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