lasso_reg_ad_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge, RidgeCV\n",
    "from sklearn.linear_model import Lasso, LassoCV\n",
    "from yellowbrick.regressor import PredictionError, ManualAlphaSelection\n",
    "from yellowbrick.regressor import ResidualsPlot\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "from yellowbrick.regressor import AlphaSelection\n",
    "from sklearn.linear_model import ElasticNetCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   radio      200 non-null    float64\n",
      " 2   newspaper  200 non-null    float64\n",
      " 3   sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       radio   newspaper       sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vDU4HSXc9dsnxpZ/oARYLSV0eA4uFSu/HE3bf7Zz/fIkvu+FzRpnjK7Y3sLdr15L9+w/x0/q8v2RnvPMR7du3JjS0pFttSpUKIzU19bLnLhUWxrkrfCcFe7JvLAP69Sm4oVyi+k11SDl8ksRf9wDw8+zvqNEyhqCSIa42OVl2/jNoOqlH86qT/d9vp0z1igAEh9rIPJ/u+8ANJqRZA+yJx8jcvBOA1HmLsbVoiKmEzdXGHFqC3D8uXPb4wJpVyfx1OzidkJND5tZdWGtE+ST2QuX4Bx8fKraJ77paNThw8LBr+8KFdM6cOUvNmlXdahNeKowWzW8kYf23bN+2jokTRmG1WgEIDw9j7Msvsu23taz/aQmdb2/vu44VA/VjbijsEAyrTLUKpCReHH7PTs8i49wflK5azrUv7dQ59v+wHQCzxUzjB9vy++rNAASH2fi/RtfxxOJxPLPqNW4b3gOL1eLbThiAtWpl7EdOuLadGZnknjuPNaqia585tCQms5kK0+OI+vp9Ks4cj7VaFQAyNmylRLsWmAKtmErYCGnegIyELb7uhs85Hc5r/viSW0Oddrudr7/+ml27dmE2m4mJieGOO+7AbC66edNmCyErKzvfvszMLEqWKOFWm63bdnL+jzTeeHMmQUGBLFr4IUNffJpXxk/hPwu+YenSVXy7dBUtmt/Ikm8+oWnz29m376BP+ib+yxoSSG62Pd++nCw7QbbgS9o279uRW567jzMHjzO335sAnNiVSFZaBj++t4SAICuxs16g9VN3sXbalz6J3yjMwUE47fmvszPLjtl2sbJ2ZmaS9t0PpM75EvvBJEr17kqFt8eSdHc/Uud9RYn2Laj2/edgsZC+NoH0tQm+7obvGWQK063MNWLECHbu3EmjRo2oX78+v/zyC6NHjy74wEJ04UI6YWGh+faFhYWSduGCW23mzl3I+FenkJmZSWrqeaZMm8UdnTsA8MzAEXy7dBUA6xM2snbdT3Ro38bLPRIBe3oWQSVt+fYFlQwhKz3zkrYJHy7n1Xr9WP/BMh5bMIaAICu/LfqRdW8tIifLTub5dNZ/sJTatzT0VfiG4cjIxFwi/3U2l7ThSL+4oC3n6ElOx03DfjAJgNQ5X2KJCMdatQplBj+OPek4B1vex6HW3TGFBBPet7tP+1AYnI5r//iSW4nvxIkTjBo1ittvv53OnTsTFxfH4cOHCz6wEO3Zs59ataq7tsuUKU3Jkjb27TvkVpuaNasRHl7K9Z3FYiE7247VaqVevX/l+1kWs4Xs7PyVo4g3nD5wnDLVy7u2baVLEmgLJuXQSde+yJoVqd7i4r/R3776iUBbMJE1KxFRtTzBYRd/oZvMZnLtOb4J3kDsh45grVrZtW0OD8NsC8aeeOzivtASBERVynecyWLGac/B1qIRaUvXQU4uzowsLqxZT3CTuj6Lv9AUpzk+u93OyZMX/8M6fvw4OTlF+z+W1Wt+pHy5SFq3agZA/6ceZumy1aSlXXCrzfBhA5kYPwqTyURQUBBPPt6HpctWExhoZe3qhbRp3RyAunVvoGXLJqxe84PvOyl+58BPOygRWYqqTfOWyTftcxt71m4h+8LFis9WOpR7Jz9FybLhAFRtcj2WQCtnE5NpM+BuOo6IxWQyERBkpWmv9uxds6UQelK0ZWzYgqVMOMGNYwAo1aMLF77/BedfKr7A62tQ+aPXsERGABB63+3knDxNztETZO87jK1N07yGZjO25o3I3nPI192QKzA53Xhl7oYNGxgzZgwBAQE4nU6cTidjx46lSZMmBf6AgMDKBbbxllYtmzJjxkRCS5Zk9+69PPzIIP4vqjJjXx5C5zt7XrHNyZOniIgozbszJ1E35gYcTidLl65m2PBXsNvt3NqhDRMmjCI4OIisrGzGjJnEN0u+K7R+Zhz7vtB+9t91OuUsfZ9+EYCDiUeIqlyRAIuF96dNoHzZyEKO7srG3Vh0hvarNrmeLq8+SlCJYE7tO8bCF96hVKUytH+hO7P7TASgae9bafbQrWAykZOZzXeTPmPfut8ICS/J3fGPUT46CqfDyd61W1gxYR659txC7lWeh0qeLriRjwQ3iqHsmIGYS9jIPpBI8vDXCKhYjohn+nD8iZEAlOrTjbD77wCnk9zkM5wa/zb2A0lYykZQdvRArDX/DxwOsrb/zqm4t3BmXDok7Ws1ty/32rlP337t9zNHLl3nwUiuzq3E96c/l/eXKlWqgJYXFWbi8xdGSnxGVZQSX3FWlBJfceXVxNfxHyS+5b5LfFdd1TlmzBjGjh3Lvffei8lkuuT7//znP14LTEREjMXXi1Su1VUT3zPPPAPAtGnTfBKMiIgYV7FIfG+88cYVv3M6ncTHx3s8IBERMaZikfg6duwIwOrVqwkICKBJkyY4nU4SEhIIDAz0SYAiImIQzkunxIqiqya+tm3bAvDZZ5/xzjvvuPZ37tyZJ5980quBiYiIeINbjyw7ceIE69ato169ephMJrZv387x48cLPlBERPxGsRjq/NPEiROZMWMGr7/+Ok6nkxo1amh+T0RE8nE6isFQ559q167NlClTXNt2u51x48YRFxfnrbhERMRgvFnxTZ06lfXr15Odnc3YsWOpW/fiI+BOnDjBkCFDyMzM5IYbbigwN7n1yLIvvviCNm3aEBMTQ8OGDWnSpAkXLlz+PVQiIuKfnE7TNX+uJiEhgW3btjF//nzi4+MvGXGcPHkyAwYM4IsvvsBsNnP06NGrns+txPfZZ5/x3Xff0bBhQzZt2kR8fDx16tRx51AREfET3no7w4YNG2jfPu+9p7Vr1yY5OZmMjIvPTd2xYwfNmuU9c/nll1+mcuWrPzHMrcRntVoJCgoiNzcXp9NJp06dWLVqlTuHioiIn3A6TNf8uZpTp04RERHh2o6IiOD06bzH250/f54SJUrw6quv0rNnT9544w0KehKnW3N8NWvW5KOPPqJJkyb06tWLSpUqFfm3M4iISPFgtVrzbTudTtdjNLOzs9m7dy9vvvkm5cuX54knnmDt2rW0a9fuiudzK/EdO3aMMWPGYLVa2bBhA6mpqbRs2fIfdENERIob91958PeULVuWM2fOuLZTUlKIjMx7m0vp0qWpUqWKa3jzpptuYt++fVdNfG4NdYaFhdGnTx/Gjx/PunXr2LJlC9OnT/8n/RARkWLGW0Odbdq0cU2v7dixg6ioKIKDg4G8l4RXqlSJpKQkALZu3Ur16tWveC5ws+K7+eZrf9WEiIj4B2/dxxcTE0N0dDRdu3bFYrEwfvx4Fi5cSGhoKLfeeivDhw/npZdeIiMjg+uuu861EOZK/tb7+K6F3sfnfXofn/fpfXy+offxeZ8338d3sP6t13xs9a2+e5m3WxWfiIhIQYrVk1tEREQKUtCN6EWFW4tbREREigtVfCIi4hHF6u0MIiIiBXEYZKhTiU9ERDzCKHN8SnwiIuIRWtUpIiJ+xbt3hXuOEp+IiHiEUSo+3c4gIiJ+RRWfiIh4hFZ1ioiIX9GqThER8Sta3CIiIn5FQ50iIuJXNNQpIiJ+xShDnbqdQURE/IoqvmJAbwf3vtEbxxV2CH7hjob9CzuEYm+FF8+tOT4REfErmuMTERG/oopPRET8ikHWtijxiYiIZ6jiExERv2KUOT7dziAiIn5FFZ+IiHiEo7ADcJMSn4iIeIQTYwx1KvGJiIhHOAyyrFOJT0REPMKhik9ERPyJhjpFRMSvGGVxi25nEBERv6KKT0REPEJDnSIi4leMMtSpxCciIh6hxCciIn5FQ50iIuJXHMbIe0p8IiLiGUa5gV23M4iIiF9RxSciIh5hkEd1KvGJiIhnaFWniIj4FYfJGHN8SnwiIuIRGuoUERG/YpShTq3qFBERv6KKT0REPEI3sIuIiF8xyg3sSnwiIuIRWtwiIiJ+RUOdIiLiV4yyqrNYJ772t7Rm6tRXsIWEcPDgYfo8PJCjR4+71SY2thtvTR3P8RPJrrYjR01g06bfWPrtvHzniKpSiZ69+vPNku980q+irEbLOtwR9zDW4EDOJiWz4Nl3OH8iJV+bOp2b0nZgNyxWC+ln01g88gOSf0+i3j0tuTPuYf44dc7VduWkz9i1fKOPe2F89pwcpsz8kI/nLWTll7OpUK5sYYdkOA1bNWTAuP4EhQRxPPEEEwdO4vSJ0/naNO/QjIeGPERgUCB/nPuDqcOmcXD3QQKsATwd15/6N9XDZDaz5YctvD16Ork5uYXUG98wylBnsb2doUQJG5/Mmc7jj79AjVpNWbZ8DTNnTHS7TXipMD77fDExdW92fb76ahlJScfy7et0ew+Sjhxj5arvC6ObRUqgLYju0wbw1YuzeLPlIPau3cpdEx7N1ya8SlnuHNeXTx59nWnth7B9SQJdJ/UDIDjMxrZv1vNW+yGuj5LetRk4dCwhQUGFHYZhBduCGf72UN4YMplezfvwy5pfeHbiwHxtIspFMHjyYCYMiOfRto+x9qu1PDdpEAAP9L+fsNKhPNbucR7v8ATVb6hO556dC6MrchnFNvG1a9eS/fsP8dP6vF+cM975iPbtWxMaWtKtNqVKhZGamlrgz5nw6gjGvzqFzMxM73TEQKrfVIeUwydJ/HUPAD/P/o4aLWMIKhniapOTZec/g6aTejTvL+f932+nTPWKAASH2sg8n+77wIuhJ/vGMqBfn8IOw7AatGzAsUPH2blxJwCLP/6ahq0aYitpc7Vx5OYS/0w8iXsTAdiyfiuVq1cGYPOPW/h3/Ac4HA7sWXa2/7ydKv//u+LMYbr2jy8V28R3Xa0aHDh42LV94UI6Z86cpWbNqm61CS8VRovmN5Kw/lu2b1vHxAmjsFqt+X5GTEw09er9i/nzF3m9P0ZQploFUhIvDg1np2eRce4PSlct59qXduoc+3/YDoDZYqbxg235ffVmIK/i+79G1/HE4nE8s+o1bhveA4vV4ttOFBP1Y24o7BAMrXL1SpxIvDgtkpmeyfmzf1CxakXXvnNnUtm49lfXdtN2Tdi9eTcAOzfu5NihvOMjykfQ9Jam/LzmFx9FX3gc/+DjSwUmPqfTyRdffOGLWDzKZgshKys7377MzCxKlijhVput23by3cr/0rZdN1q26kLTpg0Z+uLT+doOePoRpk//EKfTKCPb3mUNCSQ3255vX06WnSBb8CVtm/ftyLDN71K1yfWseHUuACd2JbLv+238u3scs7qOoUqDWrR+6i6fxC7yV8EhwWT/z79le1Y2ISUu/bcMeRXivf26MTPuvXz731jwOnN++piElQn8uu7Xyx5bnBSbxGcymdi8eTMHDx70RTwec+FCOmFhofn2hYWFknbhgltt5s5d6BrCTE09z5Rps7ijcwdXu6CgIB64/24WfbXUux0xEHt6FkF/GQoCCCoZQlb6pcPACR8u59V6/Vj/wTIeWzCGgCArvy36kXVvLSIny07m+XTWf7CU2rc09FX4Ii6Z6ZmU+J9/y7ZQGxkXLv23fFPHFrw4ZTBjHnmZpH1J+b574d7BdG/wAJWrV+bR4Y94M+QiwWm69k9Bpk6dyoMPPki3bt3Ytm3bZdu88cYb9O7du8BzuTXU+fPPP3PHHXfQuHFjWrRoQfPmzWnRooU7hxaaPXv2U6tWddd2mTKlKVnSxr59h9xqU7NmNcLDS7m+s1gs+f4CbN2qKdu37yY5Of8qL392+sBxylQv79q2lS5JoC2YlEMnXfsia1akeot/ubZ/++onAm3BRNasRETV8gSHXfxlYzKbybXn+CZ4kb9I2n+ESn+ZkwsrHUawLZhjh47la9ewVUP6j32K4T1HsnvL7679zTs0o2zFSADS/0hn+WcruPHmxr4JvhB5q+JLSEhg27ZtzJ8/n/j4eOLj4y9ps2/fPn75xb3hZLcS38qVK9m5cye//vor69evJyEhgfXr17v1AwrL6jU/Ur5cJK1bNQOg/1MPs3TZatLSLrjVZviwgUyMH4XJZCIoKIgnH+/D0mWrXcc2adKQ/QcOIxcd+GkHJSJLUbVpNABN+9zGnrVbyP7LX8m20qHcO/kpSpYNB6Bqk+uxBFo5m5hMmwF303FELCaTiYAgK017tWfvmi2F0BPxd1t+2kLpyHDqNosB4K6HuvDLml/IuJDhahMUHMTgN55n3JOvcHhP/t8FLW5rQe/ne2MymTCZTNx0W3MO7DLWqNm18Fbi27BhA+3btwegdu3aJCcnk5GRka/NxIkTef75592K0637+Pbs2cPEiRO5cOEC8+fP5+OPP+bGG2+kTp06bv2QwpCZmcmDPZ5gxoyJhJYsye7de3n4kUE0ubEBY18eQuc7e16xDcCLQ8fx7sxJ7NrxPQ6nk6VLV/PGmzNd569QoRzHj58orO4VSTlZdj7vP40urz5KUIlgTu07xsIX3qFy/Rq0f6E7s/tMJHHjHv47fTF9540Ak4mczGw+HzCNrLQMlo+fy93xjzFwzes4HU72rt3Cj7OWFHa3DOd0yln6Pv2ia7vvgKEEWCy8P20C5ctGFmJkxpGdmc0rT41n0ISB2EraSNyXyKRnX+f6BrV5aPBDjOg1khYdW1CqTClenDwk37EvdB/CrPHvM/DVAXyw7n0ADuw6yFsj3iqMrhQLp06dIjo62rUdERHB6dOniYqKAmDhwoU0a9aMSpUquXU+k9ONlRm9e/dmzJgxjB07ljlz5rBv3z5GjRrF/PnzC/wBAYHFfwlvYRtRqW1hh1Dsjd44rrBD8At3NOxf2CEUeyuSlnnt3G9F9brmY59J+uSK340dO5bmzZvTsWNHAO6//37efPNNqlSpwrlz53j22Wd5//33OXHiBMOHD2fOnDlX/VluVXwWi4VatWq5tmvVqoXFomXmIiJykbfuxytbtixnzpxxbaekpBAZmTd6kZCQwKlTp4iNjSU7O5vExEReffVVRowYccXzuZX4wsPDWbBgARkZGWzevJmVK1dSpkyZf9gVEREpTrx1W0KbNm2YPHkysbGx7Nixg6ioKIKD824t6dSpE506dQLgyJEjDB8+/KpJD9xMfOPHj2f27NmUK1eO999/n/r169O/v4YkRETkIm8lvpiYGKKjo+natSsWi4Xx48ezcOFCQkNDufXWW//2+dxKfMHBwURHRxMUFITJZKJmzZrYbLaCDxQREb/hzUd5DBmSfxHR9ddff0mbKlWqFDi/B27ezvDss8+yePFinE4nubm5LFiwgOeee87NcEVExB8Y5VmdblV8KSkpfPrpp/n2Pfjgg14JSERExJvcqviio6PZuXOna3vnzp1F+h4+ERHxPaM8q9Otim/FihV8+umnhITkvV4mIyOD8PBwlixZgslkKvJPcREREe8zyuP63Up869atw2zOXxz+9NNP3HTTTV4JSkREjMdhkNTnVuI7evQoc+fO5dy5cwDY7XZ+/fVX1qxZ483YRETEQHw9ZHmt3JrjGzZsGLVr12bHjh20bt2anJwc4uLivB2biIgYiPMffHzJrcQXEBBA165dKVWqFJ07d2bKlCn8+9//9nZsIiJiIMVqcYvT6eSHH34gLCyMefPmUbVqVZKTk70dm4iIiMe5VfG99tprhIeHM2LECLZu3cqcOXMYOnSot2MTEREDKVY3sEdGRrJr1y5++eUXoqOjqVGjBq1bt/Z2bCIiYiBGWdXp9iPLvvrqKxwOBzk5OXpkmYiIXMIoi1v0yDIREfGIYnU7gx5ZJiIiBXHgvOaPL/2tR5bZbDacTqceWSYiIpcwxgzfNT6yzOFwXPIIMxERESNwK3v95z//Ye7cuWRnZ/PAAw/QsmVLPvroIy+HJiIiRmKUG9jdSnzz58/nvvvuY+nSpdStW5cffviBZcuWeTs2ERExEKPM8bmV+AIDAwkMDGTFihXcddddWCwW7Ha7t2MTEREDMcrtDG4lvpiYGDp06EBGRgb16tXj008/pVq1al4OTUREjMQoQ51uLW7p1KkT/fv3JyIiAoC2bdty//33ezUwERExFqdB1nW6lfgWL17Mrl27CAsL48Ybb6RZs2aUL1/e27GJiIiBGOUGdrcS35/v3ktLS2PDhg3MmDGDTZs2sWnTJq8GJyIi4mlu38C+efNmEhMTCQgIoHHjxjz66KPejk1ERAzEKA+pdivxTZ48mfLly9O5c2caNWpErVq1vB2XiIgYjDHSnpuJb+nSpZw9e5ZNmzaxaNEi9uzZA8B7773n1eBERMQ4ilXFl5KSwpYtW9i6dSu7du0C4F//+pdXAxMREWMpVotbBgwYQNOmTWnevDlPPfUUISEh3o5LREQMxii3M7h1A/t7772HzWZj7dq1hISEkJCQwPnz570dm4iIGEixuoF92LBhtGjRgtWrVwN5Q58vvPACs2bNKvDY8691+WcRSoGOzzpY2CEUe3c07F/YIfiFJZtnFHYI4gfcqvjS0tLo2bMnVqsVgM6dO5OZmenVwERExFic/+B/vuRWxQeQlJSEyWQC8t7P53AYZRpTRER8wShZwa3EN3r0aF5++WX27NlDy5YtiY6OZty4cd6OTUREDMThLEaLW5YtW+a6jSE3N5cdO3bQs2dPrwYmIiLGYpTXErlV8S1fvpwVK1ZQsmRJb8cjIiIGVaxuYI+OjiYwMNDbsYiIiIEZ5T4+txJfZmYmHTt2pE6dOlgsFtf+qVOnei0wERERb3Ar8fXo0YNevXp5OxYRETGwYrWqs0WLFt6OQ0REDK5YzfGJiIgUpFjN8YmIiBSkWA11ioiIFMRpkBvYlfhERMQjjDLH59aTW0RERIoLVXwiIuIRmuMTERG/olWdIiLiV4wyx6fEJyIiHqFVnSIi4lc0xyciIn7FKHN8up1BRET8iio+ERHxCC1uERERv6LFLSIi4ldU8YmIiF/x5uKWqVOnsn79erKzsxk7dix169Z1fffzzz/z5ptvAlC1alUmTJiA2XzlJSxa3CIiIh7hcDqv+XM1CQkJbNu2jfnz5xMfH098fHy+70ePHs3UqVOZP38+mZmZrFu37qrnU8UnIiIe4a16b8OGDbRv3x6A2rVrk5ycTEZGBiEhIQB88cUXhIWFAVC6dGnS0tKuej5VfCIiUqSdOnWKiIgI13ZERASnT592bf+Z9JKTk1m/fj2tWrW66vlU8YmIiEd4a3GL1WrNt+10OjGZTPn2nTlzhieffJKRI0dSunTpq55PiU9ERDzCW4mvbNmynDlzxrWdkpJCZGSkazstLY3HHnuMQYMG0aZNmwLPp6FOERHxCKfTec2fq2nTpg2rVq0CYMeOHURFRREcHOz6Pj4+nt69e9O2bVu34vSris8cFU1g2wfBGogz9TTZyz/AmXbO9b2lViOsLe7Kf0xEBdJnDAIgsF0PzBWqA5B7eCf2/34OBrlh01dCmjckcsTTmEKCyDlynJPDJpF78nS+NjU2L8GedNy1nbX9d5JHvIYpJJiyLz1DUN1oADJ+/JXTE2eCwyiPvvWNhq0aMmBcf4JCgjieeIKJAydx+kT+a9y8QzMeGvIQgUGB/HHuD6YOm8bB3QcJsAbwdFx/6t9UD5PZzJYftvD26Onk5uQWUm+My56Tw5SZH/LxvIWs/HI2FcqVLeyQCp23Kr6YmBiio6Pp2rUrFouF8ePHs3DhQkJDQ2nVqhWLFi3i8OHDfPnllwDceeedPPDAA1c8n/8kPmsQQZ0eJeubmTiO7yeg8W0Etu9F1ldvu5rk7ttE7r5Nrm1LrUZYajcGexbWm+4Bs4XM2S+D2UxQt2cJiGlFzrbvfd6VosoUEkz5ScM4MWgsmZt3Ev7I/ZQdM4gT/Ufna4PTQdJdj11yfOkneoDFQlKXx8BiodL78YTddzvnP1/iy24UacG2YIa/PZSXH4tj58ad3P9Ud56dOJBRD73kahNRLoLBkwfzfLcXSNybyD197+a5SYMYeNezPND/fsJKh/JYu8exWC289tkkOvfszNcff12IvTKmgUPHUif6usIOo0jx5n18Q4YMybd9/fXXu/7/9u3b/9a5/Gao01Llehypp3Ac3w9Azm/rMEdFQ2DwFQ4IwNqiC/Yf8v6CMJWpiOPoXsAJjlwcxw9gKl3RR9EbQ0izBtgTj5G5eScAqfMWY2vREFMJm6uNObQEuX9cuOzxgTWrkvnr9rwqOieHzK27sNaI8knsRtGgZQOOHTrOzo1513jxx1/TsFVDbCUvXmNHbi7xz8STuDcRgC3rt1K5emUANv+4hX/Hf4DD4cCeZWf7z9up8v+/k7/nyb6xDOjXp7DDKFK8NdTpaX6T+Ezh5XCm/mU4yJ4FmRcwlbr88ERAnVbkHt2L83zeMY6k37HUqA+WAAgMxhwVjSNpty9CNwxr1crYj5xwbTszMsk9dx5r1MU/EMyhJTGZzVSYHkfU1+9TceZ4rNWqAJCxYSsl2rXAFGjFVMJGSPMGZCRs8XU3irTK1StxIvHiMHFmeibnz/5BxaoXr/G5M6lsXPura7tpuybs3pz3b3Xnxp0cO5R3fET5CJre0pSf1/zio+iLl/oxNxR2CHKNCkx8q1evZtGiRTgcDp5//nluu+02liwx4NCTNRBy7fl2OXPsmKxBl20e0KAtOZtXu7Zztq6FACshj79OSL9JOFNPkXvwN29GbDjm4CCc9v+5xll2zLaQi9uZmaR99wNnXp9FUpfHSP9xIxXeHgsWM6nzvsIUZKXa959Tbd18cpKOk742wdfdKNKCQ4LJzs5/je1Z2YSUuPzIRYOWDbi3Xzdmxr2Xb/8bC15nzk8fk7AygV/X/XrZY0X+LgfOa/74UoGJ7+2336Zdu3asXLkSk8nE/PnzmTNnji9i8yx7FgSG5NtlCgzGac+6pKm5Ui3IzcV59mL1Ym19L87UU2S8+zwZ7w7GZA0koPFtXg/bSBwZmZj/MqwJYC5pw5Ge4drOOXqS03HTsB9MAiB1zpdYIsKxVq1CmcGPY086zsGW93GodXdMIcGE9+3u0z4UdZnpmZQomf8a20JtZFzIvKTtTR1b8OKUwYx55GWS9iXl++6FewfTvcEDVK5emUeHP+LNkMWPFJuhTpvNRqlSpVi1ahUPPvggERERhnn1xF85zp7EHF7u4o7gEmANwnku+ZK2lqp1LqnmLP93Azl7NuatMMzJJufAb1gqa2L7r+yHjmCtenG+yBwehtkWjD3x2MV9oSUIiKqU7ziTxYzTnoOtRSPSlq6DnFycGVlcWLOe4CZ1kYuS9h+h0l/m5MJKhxFsC+bYoWP52jVs1ZD+Y59ieM+R7N7yu2t/8w7NKFsx7/6n9D/SWf7ZCm68ubFvgpdir9hUfGXLluWhhx5i79693HjjjXzzzTeEhob6IjaPciT9jskWmlfNAQH125J7aEdeJfg/zBWq4Ug9lf/4M8exVIvJ2zCZsETdgOPMUa/HbSQZG7ZgKRNOcOO861SqRxcufP8Lzr9UfIHX16DyR69hicx7/FDofbeTc/I0OUdPkL3vMLY2TfMams3Ymjcie88hX3ejSNvy0xZKR4ZTt1neNb7roS78suYXMi5cvMZBwUEMfuN5xj35Cof3HM53fIvbWtD7+d6YTCZMJhM33dacA7sO+rQPUnw5/8H/fMnkLKB8y8nJYc+ePdSoUYPg4GB27txJVFSU28kvfeqTHgnUE8yVahF4S08IDMKZcoKsFR9hDo3A2qILWYvecrUL7jma7B8W4ji8w7XPVKIU1naxmMtUBKcDx4lDZK+Zd9nE6WvHZxWdX1zBjWIoO2Yg5hI2sg8kkjz8NQIqliPimT4cf2IkAKX6dCPs/jvA6SQ3+Qynxr+N/UASlrIRlB09EGvN/wOHg6ztv3Mq7i2cGZcO4/naU6mmghv5SEzTOgyaMBBbSRuJ+xKZ9OzrlKtclocGP8SIXiNpe3dbBr/xPCeTTuY77oXuQ8ix5zDw1QFcVzdvtOLAroO8NeItzp1JLYyuXGLJ5hmFHYJbTqecpe/TLwJwMPEIUZUrEmCx8P60CZQvG1nA0YXLGlnDa+eOKd/8mo/dftJ38/kFJr6TJ08yffp0UlNTmTp1KkuWLKFBgwZUruzeEuiilPiKq6KU+IqropT4ijOjJD4j82biq1O+2TUfu+PkBg9GcnUFDnWOHDmS9u3bu56TVqZMGYYNG+b1wERERLyhwMSXm5vLzTff7HoSdvPmzQ25uEVERLzLWy+i9bQCH1kWGBhIQkICDoeD5ORkVq5cSVDQ5e99ExER/+XrRSrXqsCKb9y4cXzzzTekpaXRr18/du/ezYQJE3wRm4iIGIjhK76MjLzl0aGhoYwcOdJnAYmIiDEZpeK7YuK74447MJlMl7zp9s/tP9+NJCIiAvi8crtWV0x8q1evvtJXLFq0yBuxiIiIgRm+4vvTtm3bmDVrFufOnQPAbreTkpLCPffc4+XQREREPK/AxS2vvPIKvXr1Ij09ncGDB9OgQQOGDx/ui9hERMRAnE7HNX98qcCKLygoiKZNmxIcHEy9evWoV68effr0oW3btj4IT0REjMLXD5u+VgUmPpvNxtKlS6lQoQKvv/46VatWdT3FRURE5E9GebhJgYnvueeeIy0tjdGjR9O5c2duvvlmxo4d64vYRETEQIxS8RU4xzdmzBiCgoLYv38/lSpVok2bNkyePNkXsYmIiIEUmxfRWq1WYmJi+O677+jXrx+dOnXyRVwiImIwRnlyS4GJz+FwMGPGDFauXEnr1q3Zvn07WVmF/w46ERGRa1Fg4nv99dex2Wy8/fbbhISEkJiYSFxcnC9iExERAzHKG9gLXNxSsWJFHn74Ydd2586dvRmPiIgYVLFZ1SkiIuIOo6zqVOITERGPUMUnIiJ+xfBvZxAREfk7jFLxFbiqU0REpDhRxSciIh6hxS0iIuJXjDLUqcQnIiIeocUtIiLiV3z9BJZrpcQnIiIeoYpPRET8ilHm+HQ7g4iI+BVVfCIi4hGa4xMREb9ilKFOJT4REfEIJT4REfErxkh7YHIaJUWLiIh4gFZ1ioiIX1HiExERv6LEJyIifkWJT0RE/IoSn4iI+BUlPhER8St+nfji4+Pp3bs3nTp14uabb6Z3794MGDCAFStW5Gs3f/58XnnllUKKsvhas2YNw4YN49SpU7z00kuFHY7I33LLLbdw4cKFwg5DroFf38A+bNgwABYuXMjevXsZOnQoq1at4uuvv+a2225ztVu6dCnPPfdcYYVpWE6nE5PJVGC7smXLEhcX54OIxJMcDgdms1//7SwG5deJ73LatGnDuHHjyMjIICQkhDNnznDixAkaNGhQ2KEZwsKFC1m3bh2pqalUrlyZ/fv3k52dTY8ePejevTu///47Q4cOpUKFCpQtWxaAI0eOMHDgQBYuXMiGDRt48803CQgIoEKFCkyYMIHAwMBC7pX3rF+/nnnz5hEUFMTu3bvp2LEjt99+O3FxcTidTkqWLMnEiRN5/PHHeeutt4iMjKRTp048++yzdOrUidGjR9OlSxdWrFjB9u3byczM5MEHH6R79+7cfPPN3Hrrrfz++++ULl2aKVOmkJKSwuDBgwHIyclh4sSJVK5c+bJts7KyGDlyJGfPnsXhcDBq1Ciio6Pp0KEDTZo0oWHDhtx///2FfAU979ixYwwePBiTyUROTg5jx45l8uTJpKenk5mZyejRo6lXr56r/cmTJxk9ejRZWVkEBATwyiuvEBkZyZAhQ0hOTiYjI4Onn36aDh06FGKv5K/059r/sFqttG7dmjVr1gCwfPlyOnXqVMhRGcvJkyd57733qFmzJvPmzWPu3LlMmzYNgBkzZjBo0CBmzpxJQMClf3e99NJLTJ48mU8//ZTw8HAWL17s6/B9ymw2s3XrVuLi4pg3bx6ffvopr7zyCnFxccyePZuWLVsyd+5cmjZtypYtW0hJSaF8+fJs2bIFgJ07d1KjRg3WrFnD/Pnz+fzzz8nNzcVisXDq1Cm6devG3LlzCQgI4Pvvvyc5OZl+/foxZ84cunfvzty5c6/Ydvbs2bRq1YrZs2czZswYXnvtNQCOHj3KY489ViyTHuT9N3/TTTfx6aefMnLkSNLS0rj77ruZM2cOgwcPZtasWfnaT5s2jb59+/Lxxx/Tu3dv3nnnHfbs2UNKSgpz587lgw8+4I8//iik3sjlqOK7jDvvvJNPPvmEzp07s2zZMs0//U0xMTEEBgaSmppKbGwsAQEBpKSkALB//37q168PQJMmTfjvf//rOu7cuXNYLBYqVaoEQOPGjdm0aZPvO+BjderUISQkBMgbPty5cyejRo0CIDs7m7p169K2bVs2bNgAQOfOnVm7di1//PEHYWFhREZGEhUVRf/+/bntttu49957AbDZbNStWxeAunXrcvDgQWrXrs0777zDe++9R2pqKnXq1Lli223btnHy5EnXHx9ZWVkAhISEULNmTR9dHd9r0aIFTz/9NGlpadx6663UqlXL9UdJZmYmNpstX/tt27Zx4MABZsyYQW5uLmXKlKF69eqcP3+eIUOG0KFDB7p06VJIvZHLUeK7jKZNm/LSSy+RlJREWloatWrVKuyQDMVqtZKQkMCGDRuYM2cOFouFRo0aAfmf3u7OY2LdmSM0uv+tfM1mM7Nnz87X9/T0dD744ANyc3O55557+PHHH9mwYQM33ngjAB9++CG//fYbixcvZu7cuXz++eeXXGuz2cxbb71Fy5Yt6dmzJ99++63rD4/LtQUYOXIkjRs3zhef1Wr17AUoYqKjo1m8eDHff/89EyZM4NixY9xzzz288cYbbN26lddff/2SY6ZMmUL58uXz7VuwYAEbN27ks88+Y9myZUyePNlXXZACaKjzMkwmE7fccgvx8fHcfvvthR2OIf05x2e1WlmxYgUOh4Ps7GyqV6/Ojh07AFwVzJ/Cw8NxOBwcPXoUgISEBGJiYnwee2G74YYbXAlpyZIlrF+/3lVl7Nmzh1q1ahEdHc28efNo1qwZR44c4dNPP6V+/fqMGDGCw4cPk5ubS0ZGBtu3bwfyqpKaNWuSmppK1apVcTqdLF++HLvdDnDZtvXr12fVqlUA7Nu3j48++sjHV6JwLFmyhAMHDtCpUyceffRRbr75ZqpWrQrAsmXLXNfsT/Xr12flypVA3pztN998w44dO/j2229p1qwZo0aNYtu2bT7vh1yZEt8VdOnShVWrVnHHHXcUdiiG1LJlS5KSkujRowdHjhyhQ4cOjBs3jqeeeorXXnuNxx9/HKvVisPhyHdcXFwczz//PLGxsdjtdr+8/iNHjuTdd9+lZ8+eLFy4kBtuuAGAf/3rX0BeRdigQQM2bdpEvXr1KFeuHJs2baJbt27ExsbyxBNPYLFYCA8PZ8GCBTzwwAMAtGrVih49ejB+/HgeeughunXrxsaNG/nxxx8v27ZXr14kJSURGxt72cqvuKpWrRovv/wysbGxzJ49m65du7rm75o0acKZM2dYtGiRq/2AAQNYtWoVPXv2ZPr06dSvX5+oqCgWLVrE/fffz5NPPsnAgQMLr0NyCb2WSKSYatas2SVVtSfaihidKj4REfErqvhERMSvqOITERG/osQnIiJ+RYlPRET8ihKfiIj4FSU+ERHxK0p8IiLiV/4fTslphTsT2bgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop([\"sales\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y = df.sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def eval_metrics(actual, pred):\n",
    "    rmse = np.sqrt(mean_squared_error(actual, pred))\n",
    "    mae = mean_absolute_error(actual, pred)\n",
    "    mse = mean_squared_error(actual, pred)\n",
    "    score = r2_score(actual, pred)\n",
    "    return print(\"r2_score:\", score, \"\\n\", \"mae:\", mae, \"\\n\", \"mse:\", mse, \"\\n\", \"rmse:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_model = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = lasso_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8179966490329236 \n",
      " mae: 1.8549283137675237 \n",
      " mse: 5.744679831063471 \n",
      " rmse: 2.3968061730276546\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "accuracies = cross_val_score(estimator=lasso_model, X=X_train, y=y_train, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7416571484274538"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracies.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "alpha_space = np.linspace(0.1, 20, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_cv_model = LassoCV(alphas=alpha_space, cv=10).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv_model.alpha_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XIaKNP2QymdhxiIjICElNRBs3bpzYEazKr5cqrmcPeMhf5CRERFQXkirac+bMETuCVfn1SkXRHtiGRZuIyBZweFyiBEHAgcuZaObhjNa+7mLHISKiOpBU0Z4/fz7mz58vdgyrcD7rLrKK7mFAa17PJiKyFZIq2vHx8YiPjxc7hlX49XLF0HhEaw6NExHZCkkVbfqfXy9nAGDRJiKyJSzaElRxPTsLAe7OaNfUQ+w4RERURyzaEnQxuxAZhaUY0NqP17OJiGwIi7YE7b9UMTQ+gEPjREQ2RVL3aQcHB4sdwSoc+HMS2sDWASInISKi+pBU0ebM8T+vZ1/Jgp+bE9r78Xo2EZEt4fC4xFzOKcStghLen01EZIMkVbR37tyJnTt3ih1DVPr7s/m8cSIimyOp4fHKZ49HR0eLnEQ8B/j+bCIimyWpnjYBiVcy4eviiEf8vcSOQkRE9cSiLSHXc4twPa8Y/R7yg1zO69lERLaGRVtCDlypGBrno0uJiGwTi7aEVN6f3f8hXs8mIrJFLNoSknglEx5OSnRp5i12FCIiagBJzR6X8u1et++W4GJ2IYZ1CIKDnH+rERHZIkkV7aCgILEjiEZ/qxeHxomIbJakulz5+fnIz88XO4YoEq9UXM/mS0KIiGyXpHraERERAICUlBSRk1jegSuZcFE5IKy5r9hRiIiogSTV05aq7KJ7OJNRgD7BTaF04CknIrJV/A0uAYlXKx9dyqFxIiJbxqItAZXXs/vzJSFERDaNRVsCDlzOgspBjl4tm4gdhYiIGoFF284VlKpxKj0XvYKbwEnpIHYcIiJqBEnNHp8/f77YESzut2t3IAjAAA6NExHZPEkV7TFjxogdweL4vHEiIvvB4XE7d+T6HchlMvQObip2FCIiaiRJFe1x48Zh3LhxYsewmHKtDslpOegY4Al3J6XYcYiIqJEkNTx+7tw5sSNY1JnMfJSotejJWeNERHZBUj1tqUm6ng0A6MGiTURkF1i07dixGzkAwPuziYjsBIu2HTt6IxsuKgc84u8pdhQiIjIBFm07VXhPgzOZ+Qhv7gsFXxJCRGQXJDURbfDgwWJHsJjktBwIAq9nExHZE0kV7ZUrV4odwWIqr2dz5jgRkf3guKmdSrpRMXOcRZuIyH5IqmivXr0aq1evFjuGRRy7kY0Ad2e08HIROwoREZmIpIr2hg0bsGHDBrFjmF16QQnSCkrQs6UvZDKZ2HGIiMhEzHpNe+XKlUhKSoJGo8HkyZPRs2dPzJw5E4WFhQgICMCKFSugUqnMGUGSjnJonIjILpmtaB87dgznzp3Dtm3bkJ+fjxEjRqBPnz4YPXo0oqOjsXz5csTHx0vyzVvmxqJNRGSfzDY83q1bN3z44YcAAA8PD2g0Ghw5cgSDBg0CUHH71cGDB811eEk7diMHMhkQ3sJX7ChERGRCZivaCoUCrq6uAIDt27cjIiICpaWlcHJyAgD4+PggOzvbXIeXLK1Oh2M3c9DezxOezrz0QERkT8x+n/bevXvx1VdfYePGjUhMTNQvFwShTpOkUlNTTZZFLq/4GyU5Odlk+xRLTW24UlCGwjINHnKxnXbaSs66YFusE9tindiW+jNr0U5MTMTHH3+MDRs2wMPDA66urigtLYWzszOys7Ph5+dndB+hoaFwdHRsdJbk5GScPHmy0fuxBsnJyQgLCzO47vTRSwAuY1j3hxEW9rBlgzVAbW2xNWyLdWJbrBPbYlhZWVmtnVWzDY8XFhZi2bJlWLduHby9vQEA/fv3xy+//AIA2LNnDyIiIsx1eMnSPwmtBSehERHZG7P1tHfu3ImCggJMnz5dv2zZsmWYPXs2Nm7ciJCQEERHR5vr8AYdO3YMANCjRw+LHteSjt3MhqNCjs7NvMWOQkREJma2oj127FiMHTu22vItW7aY65BGTZo0CQCQkpIiWgZzKivX4vfb+egW5A0l3+xFRGR3+JvdjqTezodGq0P35rzVi4jIHrFo25HjaRXXs8NYtImI7BKLth05UVm0W/iInISIiMyBRduOJN/MhaNCjkf8vcSOQkREZsCibSfKyrVIzchH12Y+nIRGRGSnzP5ENGuyefNmsSOYze/6SWgcGicisleSKtpdu3YVO4LZHL9ZcT2bM8eJiOwXx1HtROUkNL7Zi4jIfkmqaIeHhyM8PFzsGGZxIi0XTgoHPOLvKXYUIiIyE0kNj2s0GrEjmMU9jRa/385DWHNfKDgJjYjIbvE3vB34/XYeynUCwjg0TkRk11i07UByWi4AcOY4EZGdY9G2A8k3+fhSIiIpYNG2AyfScjgJjYhIAiQ1Ee3VV18VO4LJ3dNUPAktvAUnoRER2TtJFe3XXntN7Agmd/rPSWh8qAoRkf2rtWinp6dj48aNSExMxO3btwEAgYGB6N+/P2JjYxEUFGSRkFSzZL6Ok4hIMmos2tu3b8emTZsQExODZ599FoGBgZDJZEhPT8ehQ4fw8ssvIzY2Fk8//bQl8zbK1KlTAQBr1qwROYnp6Ceh8XWcRER2r8aifeXKFXz//fdQKKp+5KGHHsJDDz2EsWPHYuXKlWYPaEoHDhwQO4LJVT4JrYMfJ6EREdm7GmcuzZo1S1+w9+zZg02bNgGoKOZarRZKpRKzZs2ySEgyrERdjtSMfHRv7sNJaEREEmD0N/2SJUvw448/Ij4+HgCQkJCARYsWmT0YGXfqVi60OoEvCSEikgijRfvs2bNYtWoVXF1dAVTMwP7jjz/MHoyMq3wdJ4s2EZE0GC3acnnFR2QyGQBAq9VCp9OZNxXVydEb2QCAHi2biJyEiIgsweh92p06dcLcuXORlZWFjRs3Yu/evejVq5clsplcly5dxI5gUsdv5sDTSYk2vu5iRyEiIgswWrRnzpyJ3bt3w9XVFRkZGXjhhRfw2GOPWSKbyf373/8WO4LJ5JeqcTG7EIPbBkAul4kdh4iILKDWoi0IAtauXYvXXnsNQ4cOtVQmqoPK69kcGiciko5ar2nLZDLcuHEDV69etVQes/riiy/wxRdfiB3DJI7frLiezUloRETSYXR4/MyZMxg+fDg8PT2hUqkgCAJkMhn2799vgXimtXz5cgBATEyMyEka71hlT5tFm4hIMowW7U8++cQSOaiejt3IQYC7M4I8XcSOQkREFmK0aAcEBCA+Ph6pqamQyWTo2rUrhg8fbolsVIPsUg1uFZTgiY7N9bfiERGR/TNatBctWoT8/Hz9bV4//vgjTp06hXnz5pk9HBl2NuceAA6NExFJjdGifenSpSqTt2JiYuzimrAtO5tTCgAIb8GZ40REUmL0iWjl5eXQarX6r3U6HZ+IJrKzuZVFmz1tIiIpMdrTHjhwIEaPHo3evXsDAJKSkmz2nu3ffvtN7AiNJggCzubcw0O+bvB1dRQ7DhERWZDRoj1lyhT07dsXKSkpEAQBixYtQufOnS2RzeTc3NzEjtBoV3KKcFetxTAOjRMRSY7Rop2ZmYmUlBS88MILAIAPPvgA/v7+8Pf3N3s4U7t27RoAoFWrVqLmaIxjNytfEsKhcSIiqTF6TXvmzJnw9PTUf/3www9j1qxZZg1lLiNHjsTIkSPFjtEofB0nEZF0GS3aOp0OTz75pP7r6OjoKhPTyLKO38yBXAZ0D/IROwoREVmY0aKtVCrx66+/oqSkBEVFRdi9e7f+HdtkWVqdDifSchHi4QhXR6XYcYiIyMKMXtNesGABFi9ejDfffBMA0K1bN7z77rtmD0bVXc4pQrG6HO2auYodhYiIRGC0aAcHB+Ozzz4DUDFUXlJSYhezsG3RqVu5AIB23k4iJyEiIjEYHef+8ssvsXXrVqjVaowaNQr9+/fH+vXrLZGNHpCSngeARZuISKqMFu1t27Zh3Lhx2Lt3Lzp06ICkpCTs2bPHEtlMbsWKFVixYoXYMRqssmi39eZDVYiIpMjo8LibmxsUCgUSExMRHR0NlUoFR0fbLBqRkZFiR2iUlFu5aOHlAi9Ho6eNiIjskNGetkwmw8KFC3HkyBH06tULR44cgUajsUQ2uk9WYSnS75aiSzPe6kVEJFVGi/aKFSvQqlUrrFu3DiqVCjk5OVi4cKEFopnesGHDMGzYMLFjNEjl0HiXZt4iJyEiIrEYHWf18/NDbGys/uvHH3/cnHnMKj09XewIDaYv2kHegCZb5DRERCQGsz4l5cKFCxgyZAi2bt0KAIiLi8OoUaMwYcIETJgwAfv37zfn4e1K5e1eXTk8TkQkWWab0VRSUoK4uDj06dOnyrIlS5agQ4cO5jqs3UpJz4O7oxIhPm44eV3sNEREJIZae9qCIGDx4sUN2rFKpcJnn30GPz8//bLi4uIG7UvqSjXlOJ91F12aeUMul4kdh4iIRFJrT1smk8HJyQmHDx9Gt27doFKp9OuMPX9coVBAoai6++LiYqxevRqFhYXw9/fH/Pnz4eXl1fD0EnEmowA6QeAkNCIiiZMJgiDU9oHw8HAUFxfj/o/JZDKcO3euTgdYs2YNvL29MX78eOzZswchISFo06YN1q1bh4yMDLzzzjsGtysrK0Nqamo9mmLchg0bAAATJ0406X7N7btLeXjv6G3M7RWIka1ZuImI7F1oaKjBZ6IYvaZ9/Phxk4W4/+EmgwcPxoIFC4xuU1Pw+kpOTsbHH3/c6P2IYdP1owBuY0Sfbghr4Yvk5GSEhYWJHcsk2BbrxLZYJ7bFOpmyLcY6rEaLdlFRETZt2oTU1FTIZDJ069YNzz//PJyc6v/86ylTpuDtt99G8+bNcfToUbRt27be+5CilFu5cJDL0DHAU+woREQkIqNFe+7cuQgMDMSzzz4LQRCQmJiIefPmGX2Gd2pqKpYvX45bt25BoVAgISEB48ePx/Tp0+Ho6AhXV1csXbrUZA2pi8pXitY0JG+NdDoBKbfz0N7PA85KPr6UiEjKjFaB3NxcrFq1Sv/1wIEDMX78eKM7Dg0NxZYtW6otj4qKqmdE0/nmm28A2FbRvpJbiKKycj6+lIiIjD9cpbS0FCUlJfqvi4uLUVZWZtZQ9D+nblU8Ca0rZ44TEUme0Z72uHHjMGzYMHTu3BlAxbD3tGnTzB6MKqSkVzwJrUsQe9pERFJntGiPGTMGjz76KM6cOQMAmDdvHvz9/c0ejCrwRSFERFSpxuHxadOmoaCgAAAQGBiIIUOGYMiQIfqCnZ+fj6lTp1ompYSl3MpDMw9nNHWr/2x9IiKyLzX2tMeNG4dnnnkGAwYMQL9+/RAYGAgAuH37NhITE3HgwAGbe0Vns2bNxI5QL9lF95BWUIJhHYLEjkJERFagxqLdt29ffPvtt/j666/x+eefIz09HYIgIDAwEP369cOOHTvg5uZmyayNtmvXLrEj1MuJP9/s1Z3Xs4mICEauabu4uOD555/H888/b6k8dJ8TaTkAgO7NWbSJiMjM79O2Nnv27MGePXvEjlFnJ9L+7Gk39xU5CRERWQNJPWLrzTffBACkpKSInKRuTqTlwtfFES28XMSOQkREVsBoT/v333+3RA56QF5JGa7mFqF7cx/IZHyHNhER1aFof/jhhxaIQQ86WTkJjdeziYjoT0aHx/38/DB+/Hh06dIFKpVKv/xvf/ubWYNJHa9nExHRg4wW7aCgIAQF8T5hS/tf0WZPm4iIKhgt2q+//jqKi4tx9epVyOVyhISEwNnZ2RLZJO1EWg68nFUI8bGte+GJiMh8jBbthIQEvPvuu2jWrBl0Oh2ysrKwePFiREREWCKfSX3//fdiR6iTu/fUuJhdiEFtAjgJjYiI9IwW7Q0bNiA+Ph6+vhXXVjMzMzFt2jSbLNqtWrUSO0KdVL6OsxuHxomI6D5GZ4+rVCp9wQYAf39/KJVKs4Yyl6KiIhQVFYkdwyg+CY2IiAwx2tN2c3PD+vXr0b9/fwBAYmKizT1zvNKjjz4KwPofrqJ/5jhnjhMR0X2MFu0lS5Zg1apVmDVrFgRBQPfu3fHee+9ZIptknUjLhbujEm183cWOQkREVqTWoi0IAk6fPo13333XUnkkr7hMg/NZBegf4ge5nJPQiIjof2q9pi2TybBt2zaUl5dbKo/knUrPgyBwEhoREVVndHjcyckJ0dHReOSRR6o8Ee399983azCpOsknoRERUQ2MFu2IiAibvL3LVuknoQWxp01ERFUZvaadkZGB1157zVJ5zGrWrFliRzDqRFoOXFQOeNjPQ+woRERkZWot2jKZDDdu3MDVq1cREhJiqUxmExMTI3aEWpVqynE2swC9WjaBg9zoLfRERCQxRofHz5w5g+HDh8PT0xMqlQqCIEAmk2H//v0WiCctp27lQasT+FAVIiIyyGjR/uSTTyyRwyKef/55AMC///1vkZMYdvRGNgCgZ8smIichIiJrZHQMtkmTJtizZw+2bNmCoKAgZGRkwMvLywLRTC8lJcWqn4Z25PodAECvYBZtIiKqzmjRnjdvHrKysnDixAkAwMWLFzFnzhyzB5Oiozey4eviiNZ8EhoRERlgtGhnZGRg5syZcHR0BACMGzcO2dnZZg8mNZmFpbiWW4yewU34Ok4iIjLIaNEWBAEA9IWkpKQEZWVl5k0lQUnXK/4Q6sXr2UREVAOjE9GGDBmCF198EWlpaVi8eDESExOt/tYpW1Q5CY3Xs4mIqCZGi3ZsbCzCwsJw4sQJyGQy/OMf/0BoaKglspncgAEDxI5QI84cJyIiY4wWbQDo1KkTOnXqZO4sZrdmzRqxIxik1elw9EYO2vt5wMtZZXwDIiKSJD52ywqcz7qLwjINe9lERFQrSRXtTz75xCofFlN5f3ZPXs8mIqJaSKpor127FmvXrhU7RjWV17N7t2wqchIiIrJmkira1uro9Rw4Kx3QKdBL7ChERGTFWLRFVlSmQWpGPsKa+0LhwNNBREQ1Y5UQ2fGbOdAJAu/PJiIio1i0Rcb7s4mIqK7qdJ+2vVAqlWJHqObIn48v7c2eNhERGSGpon38+HGxI1QhCAKSrmejmYczmnu5ih2HiIisHIfHRXQzvwQZhaW8P5uIiOpEUkX71KlTOHXqlNgx9A5dywLA+7OJiKhuJDU8/sILLwAAUlJSRE5S4eCViqLdv7WfyEmIiMgWSKqnbW0Sr2TBWemA7kE+YkchIiIbYNaifeHCBQwZMgRbt24FAOTk5GDixIl45plnMG3aNKjVanMe3qrllpQhNSMffYKbQqVwEDsOERHZALMV7ZKSEsTFxaFPnz76Ze+//z5Gjx6Nr776CkFBQYiPjzfX4a1e5dB4v4c4NE5ERHVjtqKtUqnw2Wefwc/vf0Xp6NGjGDRoEABg8ODBOHjwoLkOb/UOXv3zejaLNhER1ZHZJqIpFAooFFV3X1xcDCcnJwCAj48PsrOzzXV4q5d4JRMKuQy9gzlznIiI6sais8fvfyKZIAiQyWRGt0lNTTXZ8d98800AQHJyssn22RAlGh2Sb+agg48zzv3esJnsYrfBlNgW68S2WCe2xTpZqi0WLdqurq4oLS2Fs7MzsrOzqwyd1yQ0NBSOjo6NPnZycjImTJjQ6P2Ywt4Lt6EVzmNopxCEhYXVe/vk5OQGbWeN2BbrxLZYJ7bFOpmyLWVlZbV2Vi16y1f//v3xyy+/AAD27NmDiIgISx7eavzv/mx/kZMQEZEtMVtPOzU1FcuXL8etW7egUCiQkJCAFStW4M0338TGjRsREhKC6Ohocx3eoMqZ7IcPH7bocR+UeCUTMhnwaCtezyYiorozW9EODQ3Fli1bqi03tMxSSkpKRDt2JXW5FkeuZ6NTgDe8XRo/7E9ERNLBJ6JZ2PGbObhXruX92UREVG8s2hbG+7OJiKihWLQtLPEKizYRETUMi7YFaXU6/HY1C22auCPQw0XsOEREZGMk9WrOiRMninr81Ix8FNzT4KlOLUXNQUREtklSRXvatGmiHn/fxQwAQP+HeH82ERHVH4fHLWjPhdsAgMiHA0VOQkREtkhSRXvGjBmYMWOGKMcuK9fiwJVMdPD3RJAnr2cTEVH9SWp4vPIRqmI4fO0OStRaDGnHXjYRETWMpHraYtr759A4izYRETUUi7aF7L1wGwq5DBGchEZERA3Eom0BuSVlOJ6Wgz6tmsLdSWl8AyIiIgNYtC3gvxczIAgcGiciosaR1ES0Dh06iHJcXs8mIiJTkFTR/vLLL0U57t4Lt+HppER4c19Rjk9ERPaBw+Nmdjm7EFdzi/CXtgFQOPDbTUREDSepKvL111/j66+/tugx93BonIiITERSw+NxcXEAgDFjxljsmJXXsyNZtImIqJEk1dO2tHKtDv+9eButfFzR2tdd7DhERGTjWLTN6HhaDgruaTCkXSBkMpnYcYiIyMaxaJvRz+fTAQBD2jUTOQkREdkDFm0zij+TBqWDHFF8FScREZkAi7aZ3MgrxslbuRjY2h8eTiqx4xARkR2Q1OzxX3/91WLH+uHMTQDAyNAWFjsmERHZN0kVbS8vL4sd6/vUiqI9/JHmFjsmERHZN0kNj9+6dQu3bt0y+3EKStX49XImujf3QQtvV7Mfj4iIpEFSPe3o6GgAQEpKilmPs+v8LZTrBIzoyKFxIiIyHUn1tC0lPjUNADAilEPjRERkOizaJqYu12LX+VsI9nZF50BvseMQEZEdYdE2sQNXsnD3ngZPdGzOp6AREZFJsWibWPyfs8Z5PZuIiEyNRduEBEFA/Jmb8HJWYUBrf7HjEBGRnZHU7PGlS5eadf+nbuXhZn4Jnu3WCkoH/j1ERESmJamiXXnLl7lUPlDlCQ6NExGRGbA7aCKCIOA/J6/CWemA6A5BYschIiI7JKmiPWLECIwYMcIs+z56IxuXsgsxMrQF3J2UZjkGERFJm6SGx69fv262fX9x4ioA4Lmwh8x2DCIikjZJ9bTNRaPVYdupa2ji6ojIdnx3NhERmQeLtgnsvXAbd4rKMLYrZ40TEZH5sMKYwOfJVwAAz4WFiJyEiIjsGYt2IxWVafD9mZto7euOni2biB2HiIjsmKQmoplj5vh3qTdRotbiubAQPmuciIjMSlJFOy4uzuT7/Dy5YtZ4THcOjRMRkXlxeLwRMgtLsffCbfRs6Yu2TT3EjkNERHZOUkV76dKlJn3++LaT16ATBDzXnfdmExGR+UmqaH/55Zf48ssvTbIvQRDw2ZGLUDrI8UzXYJPsk4iIqDaSKtqm9N+LGTibWYAxnVvCz91Z7DhERCQBFp2IlpqaiilTpiA4uKJn2q5dO8yfP9+SEUxmzcHzAICp/duLnISIyHLKy8uh0+lMsi+1Wm2S/ViD+rZFLpdDoah/CbZo0S4pKUFUVBTmzp1rycOa3NWcQvx4Ng09W/qiV3BTseMQEVlEYWEhHBwcGlRsHtS6dWsTJLIODWmLWq1GaWkp3N3d67WdRYt2cXGxJQ9nNh//dgGCAPy1H3vZRCQN5eXlcHBwgIuLi0n2p9FooFKpTLIvsTWkLSqVCiUlJSgvL6/XH0EW72knJyfjxRdfhEajwV//+lf06dPHYsdv2rTxveLiMg3+dfQS/N2d8HQXTkAjImnQ6XQm6WHT/zg4ONT7UoNMEATBTHmquXz5Mi5duoSoqChcv34dsbGxSEhIMPgXSllZGVJTUy0Vrc52XMzDsmO3MSm0CV7u7Cd2HCIii2ndujWUSqXYMeyGRqPB5cuXDa4LDQ2Fo6NjteUW/bOpdevW+rH/4OBgNGnSBJmZmWjRokWN29QUvL6Sk5MRFhbWqH0IgoAX//sDFHIZFo6OQKCHaYaJ6ssUbbEWbIt1Ylusk5htqZxoZaoh7eLiYri6utZ7ux07duDixYuYNWuWSXKYQkPbolar0alTpyrfU2MdVove8vXtt99i06ZNAICcnBzk5OTA39/fYsffv38/9u/f3+Dt913KwJmMAjzdJVi0gk1ERNJl0Z72kCFD8NZbb+Hnn39GeXk5FixYYNGJCH/7298AACkpKQ3a/oNfzwEAXudtXkREolq+fDlOnjwJtVqNZ599Fk8//TQOHjyIDz/8EA4ODmjSpAlWrlyJY8eOVVuWl5eHt99+G2q1Gg4ODliyZAmaN28udpPqxKJF293dHWvXrrXkIU3mt6tZ2HnuFvo/5IdefAUnEUnczB+S8XXK9QZvLwhCtTcjjukSjPefMD78r9VqERAQgP/85z8oKytDZGQknn76aWzduhWzZs1Cjx49sGvXLuTm5hpctnr1aowaNQrDhw9HQkICVq9ejffff7/BbbEkPhGtDgRBwNs/nQQAvBfdja/gJCISkYODAwoKChATE4PJkycjNzcXAPDYY49hwYIFWLt2LTp27IjAwECDy1JTU/V3LoWHh+PcuXNiNqdeOH+/DnadT8fBq1l4omNz9A3hjHEiovefCKtTr7gmDZ28BQBJSUlQqVTYsmULHBwc0L17dwDAqFGj0K9fP+zduxexsbH46KOPDC67nyAIkMttp/9qO0lFotMJmPvTSchkwOJhXcWOQ0Qkefn5+QgKCoJSqcTPP/8MnU4HtVqNjz76CM7OzoiJiUFERAT++OMPg8s6d+6MQ4cOAQCOHDmC0NBQkVtUd+xpG/Gfk1dx+nYeJoQ/hNBAb7HjEBFJXmRkJE6ePIlnn30WgwcPxpAhQxAXF4ewsDDExMTA09MTrq6ueOONN7B3795qy3r37o23334b27Ztg6OjI9577z2xm1Rnkira27dvr9fn1eVaLNidAqWDHAujupgpFRER1dWoUaNqXf/kk09W+/rBZW5ubvjXv/5l4mSWIami3a5du3p9fv2RS7iaW4Sp/dujlY+bmVIRERHVjaSuaavV6jq/Pu1WQQkWJJyCm6MCbw+2nesdRERkvyTV0+7RowcA4w9X0ekEvPTlIeSWqPHPUT3h5+5siXhERES1klRPu67+efA89l64jWEdgvBq3/oNqRMREZkLi/YDUm/nYfZPJ9DUzREbxvbhg1SIiMhqsGjfp6xciwmf/4aych3WPd0H/hwWJyIiK8KifZ+3fzqJ07fzMLl3W4wIrfl1oURERGKQ1ES0mgiCgLifT+PDA+fQtok7/jHCPt69S0RE9kVSPe0ZM2ZgxowZVZZVvgxk0c+nEeLjht2vDIGro1KkhEREZCkHDhxAVFQUIiMjsW7duho/N2fOHPTp0wfDhw83uP2IESOqbV/XfdeXpIr2Cy+8gBdeeEH/tSAImP79cby/7wzaNfXA/r8+xoeoEBFJgFarxbvvvov169fjp59+wo8//ohLly4Z/OyoUaOwfv36Grf/5ptvqmxfn33Xl6SK9v0yC0vx0peHsCbxPDoGeGLflMfQ3Kthb5whIiLLuXjxImJjYxEVFYWPPvoIcXFxOH36dL32cfr0aQQHB6NFixZQqVR4/PHH8csvvxj8bI8ePeDp6Vnj9kqlssr29dl3fUnqmvbEiRNRrC5HkyenYOPRy7hXrkW3IB/sfnkwmrg5iR2PiMimdOli+J0MU6dOxaRJkwAAr776Kg4fPlztM127dsXmzZsBAJs3b8bKlSuNPvgKAMrKyvC3v/0Nq1atQosWLTBs2DB07NgRnTt31n8mJiYGxcXF1badNWsW+vbtCwDIzMxEQECAfp2/v3+9Cn9t2zd237WRTNHW6gQkHPgNRWXlKPQbglY+rnhzYEfE9mwNZ6Vkvg1ERDbt0KFD6NChA9q2bQsA0Gg0ePHFF6t85osvvjC6H0EQqi2rz3M5atu+sfuujWSqlUYnoERdDqWDHFue64dnugRD4SDZqwNERI1Wl57x2rVrDS6/vyf84Hyj2pw9exYdO3YEUNGjdXFxQVhY1Tt+6tLTDggIQEZGhn5dZmYm/Pz86pTB2PaN3XdtJFO0nRRytPjzmnVM9xCR0xARUUOoVCp9QVy5ciU0Gk21z9Slp92pUydcu3YNN2/ehL+/P3766Sf84x//qHOO+7d3d3evsn1j910bdjWJiMhmPPHEEzh+/DiioqLQvn17dO3aFUuWLKn3fhQKBd555x1MmjQJ0dHRGDZsmH7IHQAmT56MzMxMABW3C48bNw5Xr17FgAEDsH379irbjx49usr2xvbdGJLpaRMRke0LCAjAjh07TLKviIgIREREGFz32Wef6f9/5cqVtW5fXFwMV1dXg+tMTVJFu0+fPmJHICIiajBJFe2aJkQQERHZAl7TJiIiEoGhW8OMkVTRXr9+fbVH0RERkXFyuRzl5eVix7ArWq0Wcnn9yrCkhsfXrFkDAPon9RARUd0oFAqUlpaipKQEDg4OjX5YiEajgVqtNlE6cdW3LYIgQKvVQqvVQqGoXxmWVE+biIgazt3dHSqVyiRP97p8+bIJElmH+rZFJpNBpVLB3d293seSVE+biIgap749w9qoVCqT7UtslmoLe9pEREQ2gkWbiIjIRljt8HjlVHhTTlTw9fUFUPFqN1tnD22oxLZYJ7bFOrEt1slUbamseTXdDiYTGnKjmAUUFhbiwoULYscgIiKyuHbt2hmcqGa1RVun06G4uBhKpdJk7yElIiKyZoIgQKPRwNXV1eA93FZbtImIiKgqTkQjIiKyESzaRERENoJFm4iIyEawaBMREdkIq71Pu6FWrVqFw4cPQ61WY9GiRejUqZN+3cmTJ7F8+XKUlZUhMjISU6ZMETGpcStXrkRSUhI0Gg0mT56MYcOG6dc9+eSTVW4HWLFiBfz9/cWIaVRqaiqmTJmC4OBgABW3MsyfP1+/3pbOy/bt2xEfH6//OjU1FSdPntR/bSvn5cKFC5gyZQpiY2Mxfvx45OTkYObMmSgsLERAQABWrFhR7bGMtf1sienBtmRmZmLOnDlQq9WQy+X4v//7vyrnwNi/RzE92Ja4uDicPHkSrq6uAICJEydi4MCBVbaxxvPyYDumTZuGvLw8AEB+fj66du2KuLg4/eet+Zw8+Hu4Z8+e4v6sCHbk8OHDwsSJEwVBEIQ//vhDiImJqbL+scceE9LT0wWtViuMGTNGuH79uhgx6+To0aPCpEmTBEEQhLy8PKF///5V1o8cOVKEVA2TlJQkLF68uMb1tnRe7nfs2DHhnXfeqbLMFs5LcXGxMH78eGHevHnCli1bBEEQhJkzZwo//fSTIAiCsGzZMmH79u1VtjH2syUWQ22ZPXu2vi1bt24Vli1bVmUbY/8exVJTW86ePVvjNtZ4Xgy1435z5swRTp48WWWZtZ4TQ7+Hxf5Zsavh8aSkJAwePBhAxV9qWVlZKC0tBQDcvHkTnp6eCAwMhFwux8CBA3Hw4EEx49aqW7du+PDDDwEAHh4e0Gg00Ol0+vXFxcUiJau/2rLa2nm535o1a6qNCtjCeVGpVPjss8/g5+enX3b06FEMGjQIADB48OBq56C2ny0xGWrLvHnz8NhjjwEAvL29UVRUVGUbaz1HhtpiLKs1nhdD7ah09epV5OXloWvXrlWWW+s5MfR7+MiRI6L+rNhV0b5z5w58fHz0X/v4+CA7OxsAkJWVVWWdr6+vfp01UigU+iGx7du3IyIiosqN9vn5+Zg+fTqeffZZfPDBBzU+8s4alJSUIDk5GS+++CLGjx+Pw4cP69fZ2nmpdPr0afj7+1cb+raF86JQKODk5FRlWXFxsX7Z/T83lWr72RKToba4urpCoVBAq9Xiiy++wOOPP15lfW3/HsVU03lZvXo1xo8fjzfeeAP5+flV1lvjeTHUjkqbN2/G888/X225NZ+TB38Pl5aWivqzYlfXtJVKZZWvBUHQP02ttnXWbO/evfjqq6+wcePGKsunT5+O6OhouLq6YurUqUhISMDQoUNFSlm79u3b45VXXkFUVBSuX7+O2NhYJCQkQKVS2ex5+eqrrxAdHV1tuS2dl/vdfx4MnQNbO09arRYzZ85Ez5490bt37yrravv3aG3GjRuHkJAQtGnTBuvWrcPq1avxzjvv6Nfb0nkpLS3F4cOHq+SvZO3n5P7fw4mJifrlYvys2FVPu2nTpsjJydF/nZubiyZNmgAA/Pz8qqzLzs42OHxjTRITE/Hxxx9j/fr18PDwqLIuJiYGXl5eUCqVGDBggFU/p71169aIiooCAAQHB6NJkybIzMwEYJvnBQCOHTuGPn36VFtuS+flfq6urvohPEPnoLafLWs0Z84cBAUFYdq0adXW1fbv0dpERkaiTZs2ACqGYh/892RL5+XEiRPo3r27wUdzWvM5efD3sNg/K3ZVtAcMGIBffvkFAHDmzBm0aNFCP4wREBCA8vJypKenQ6vVYt++fRgwYICYcWtVWFiIZcuWYd26dfD29q6yLj8/HxMnToRGowFQUUDatm0rRsw6+fbbb7Fp0yYAQE5ODnJycvTDyrZ2XgAgIyMDKpUKjo6OVZbb2nm5X//+/fU/O3v27EFERESV9bX9bFmb+Ph4yOVyzJgxw+D62v49WpspU6YgLS0NQMW8gwf/PdnSeUlJSUG7du0MrrPWc2Lo97DYPyt2NTweGhqK9u3b46mnnoKDgwOWLFmCHTt2wN3dHZGRkXj77bcxZcoUyGQyjBgxAoGBgWJHrtHOnTtRUFCA6dOn65f16tULDz/8MCIjI9G/f3+MGzcOSqUSHTt2tOoh2CFDhuCtt97Czz//jPLycixYsAA//vijTZ4XoOI6/P1/Xd//b8wWzktqaiqWL1+OW7duQaFQICEhAStWrMCbb76JjRs3IiQkRD/0P336dCxdutTgz5Y1MNSWnJwcODo6YsKECQAqenELFy7Ut8XQv0drGIY11Jbx48dj+vTpcHR0hKurK5YuXQrAus+LoXasWbMGd+7cQXh4eJXPWvs5MfR7eNmyZZg9e7ZoPyt8YQgREZGNsKvhcSIiInvGok1ERGQjWLSJiIhsBIs2ERGRjWDRJiIishEs2kQSlZaWZvSe+DVr1uCDDz6wUCIiMoZFm4iIyEbY1cNViMgwQRCwaNEinD9/HlqtFl26dEFsbKx+/VtvvQU3Nzekp6fj1q1bGD16NF588UUAFQ+TmTZtGi5duoSePXti4cKFuHfvHmbNmoXs7GyUlpZi6NChePnll0VqHZF0sGgTScDdu3fRsmVLLFiwAAAwdOhQPPPMM/r1Dg4OuHPnDj799FMUFBTgsccew5NPPgmg4nWKW7duhUajwaOPPoq///3vKCoqQt++fTF27Fio1Wr06dMHMTExcHNzE6N5RJLBok0kAS4uLrh9+zZiYmKgUCiQnZ2N1NTUKp/p1asXAMDT0xMtW7bEjRs3AADh4eFQKBRQKBTw8vLC3bt34e3tjVOnTuG7776DQqFAWVkZ8vPzWbSJzIxFm0gCfvjhB5w7dw6bN2+GSqXCyJEjq33m/ica63Q6/esEHRwcqn1u06ZNUKvV+OKLLwAAPXv2NGN6IqrEiWhEElBQUIAWLVpApVIhJSUFaWlpUKvVVT5z+PBh/WfT0tLQqlWrWvcXEhICmUyGhIQEqNXqavsjItNj0SaSgOjoaJw7dw4xMTHYvXs3Jk2ahGXLluHu3bv6z3h5eeGll17Cc889h9dff73aO9zvN2bMGOzcuRPPPfcc0tPTMXLkSMyePdsSTSGSNL7li4gwe/ZshIWF4emnnxY7ChHVgj1tIiIiG8GeNhERkY1gT5uIiMhGsGgTERHZCBZtIiIiG8GiTUREZCNYtImIiGwEizYREZGN+H9Wz1XlXT6KUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'LassoCV Alpha Error'}, xlabel='alpha', ylabel='error (or score)'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_yellowbrick = LassoCV(alphas=alpha_space)\n",
    "visualizer = AlphaSelection(model_yellowbrick)\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8179966490329236 \n",
      " mae: 1.8549283137675237 \n",
      " mse: 5.744679831063471 \n",
      " rmse: 2.3968061730276546\n"
     ]
    }
   ],
   "source": [
    "# Let's try our model with the optimal alpha value from yellowbrick.\n",
    "\n",
    "lasso_model_0_1 = Lasso(0.01).fit(X_train, y_train)\n",
    "y_pred_0_1 = lasso_model.predict(X_test)\n",
    "eval_metrics(y_test, y_pred_0_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "elastic_model = ElasticNetCV(alphas=alpha_space, l1_ratio=[.1, .5, .7, .9, .95, .99, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNetCV(alphas=array([ 0.1       ,  0.3010101 ,  0.5020202 ,  0.7030303 ,  0.9040404 ,\n",
       "        1.10505051,  1.30606061,  1.50707071,  1.70808081,  1.90909091,\n",
       "        2.11010101,  2.31111111,  2.51212121,  2.71313131,  2.91414141,\n",
       "        3.11515152,  3.31616162,  3.51717172,  3.71818182,  3.91919192,\n",
       "        4.12020202,  4.32121212,  4.52222222,  4.72323232,  4.92424242,\n",
       "        5.12525253,  5.32626263,  5.52727273,  5.72828283,  5.92929293,\n",
       "        6.13...\n",
       "       14.17070707, 14.37171717, 14.57272727, 14.77373737, 14.97474747,\n",
       "       15.17575758, 15.37676768, 15.57777778, 15.77878788, 15.97979798,\n",
       "       16.18080808, 16.38181818, 16.58282828, 16.78383838, 16.98484848,\n",
       "       17.18585859, 17.38686869, 17.58787879, 17.78888889, 17.98989899,\n",
       "       18.19090909, 18.39191919, 18.59292929, 18.79393939, 18.99494949,\n",
       "       19.1959596 , 19.3969697 , 19.5979798 , 19.7989899 , 20.        ]),\n",
       "             l1_ratio=[0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred_elastic = elastic_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8983361384489736 \n",
      " mae: 1.4612734376961354 \n",
      " mse: 3.2088768250528417 \n",
      " rmse: 1.791333811731594\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred_elastic)"
   ]
  }
 ],
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