lasso.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('auto-mpg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "   origin                   car name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df = df.select_dtypes([np.number])\n",
    "obj_df = df.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Jmjp1akT7ihUrNG/ePEnSo48+qmPHjunBBx/Ud999p7y8PG3atEmJiYnh/j6fT3FxPy7u5Ofna/Xq1XriiSe0ePFiXXTRRaqurlZeXl4fbwsAAMSSs3oOi0l4DgsQm3gOCxDbzslzWAAAAM4FAgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8aIOLPX19Zo5c6YyMjJkWZbWr18fcd2yrG6Pv//97z3OWVlZ2e2Y9vb2qG8IAADEnqgDy9GjRzVx4kQtW7as2+uHDh2KON544w1ZlqU77rij13mTkpK6jHW73dGWBwAAYtCwaAcUFRWpqKiox+tpaWkR5xs2bNC0adP0q1/9qtd5LcvqMhYAAEAa4D0s33zzjTZu3Kg//OEPp+3b1tam0aNHKysrS7fccot2797da/9QKKRgMBhxAACA2DSggeUf//iHEhMTdfvtt/fa7+KLL1ZlZaVqamq0atUqud1uXXPNNdq3b1+PY7xerzweT/jIzs7u7/IBAIAhBjSwvPHGG7r77rtPuxdl8uTJuueeezRx4kRdd911evvttzVu3Di9/PLLPY6pqKhQIBAIH83Nzf1dPgAAMETUe1jO1Pvvv6+9e/equro66rFxcXHKzc3tdYXF5XLJ5XKdTYkAAGCQGLAVltdff11XXXWVJk6cGPVY27bV1NSk9PT0AagMAAAMNlGvsLS1tenLL78Mn+/fv19NTU1KTk5WTk6OJCkYDOqf//ynnn/++W7nKCsrU2ZmprxeryTp6aef1uTJkzV27FgFg0G99NJLampq0iuvvNKXewIAADEm6sCyc+dOTZs2LXxeXl4uSZo7d64qKyslSatXr5Zt2/r973/f7Rw+n09xcT8u7rS2tur++++X3++Xx+PRFVdcofr6ek2aNCna8gYt27Z5UB7QjZ/+XfA3AnTldrtlWZbTZQw4y7Zt2+ki+kMwGJTH41EgEFBSUpLT5UTt2LFjvT7fBgCA7tTW1iohIcHpMvrsTF+/+S4hAABgvAH7lBD6ru3y38uO41cDSJJsW+o8+cPPccOkIbD0DZyO1XlS/9e0yukyzileFQ1kxw2T4n/hdBmAQYY7XQBglJjYyxEl3hICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4w5wuAD+wbfvHk44TzhUCADDfT14nIl4/YhiBxRChUCj8c+Ke1Q5WAgAYTEKhkM477zynyxhwvCUEAACMxwqLIVwuV/jnIxN/J8X/wsFqAABG6zgRXo3/6etHLCOwGMKyrB9P4n9BYAEAnJGI148YxltCAADAeAQWAABgPAILAAAwXtSBpb6+XjNnzlRGRoYsy9L69esjrs+bN0+WZUUckydPPu28a9as0fjx4+VyuTR+/HitW7cu2tIAAECMijqwHD16VBMnTtSyZct67DNjxgwdOnQofLz77ru9ztnQ0KDi4mKVlpZqz549Ki0t1Zw5c7Rjx45oywMAADEo6k8JFRUVqaioqNc+LpdLaWlpZzznCy+8oJtuukkVFRWSpIqKCm3btk0vvPCCVq1aFW2JAAAgxgzIHpatW7cqJSVF48aN03333afDhw/32r+hoUGFhYURbdOnT9f27dt7HBMKhRQMBiMOAAAQm/o9sBQVFWnlypXasmWLnn/+eTU2NuqGG26IePT8z/n9fqWmpka0paamyu/39zjG6/XK4/GEj+zs7H67BwAAYJZ+f3BccXFx+OcJEybo6quv1ujRo7Vx40bdfvvtPY77+YNvbNvu9WE4FRUVKi8vD58Hg0FCCwAAMWrAn3Sbnp6u0aNHa9++fT32SUtL67Kacvjw4S6rLj/lcrmGzOOIAQAY6gb8OSwtLS1qbm5Wenp6j32mTJmiurq6iLZNmzYpPz9/oMsDAACDQNQrLG1tbfryyy/D5/v371dTU5OSk5OVnJysp556SnfccYfS09P11Vdf6U9/+pNGjRql2bNnh8eUlZUpMzNTXq9XkrRw4UIVFBRoyZIluu2227RhwwZt3rxZH3zwQT/cIgAAGOyiDiw7d+7UtGnTwuen9pHMnTtXy5cv1yeffKI333xTra2tSk9P17Rp01RdXa3ExMTwGJ/Pp7i4Hxd38vPztXr1aj3xxBNavHixLrroIlVXVysvL+9s7g0AAMSIqAPL1KlTZdt2j9f/85//nHaOrVu3dmm78847deedd0ZbDgAAGAL4LiEAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8YY5XQC6sjpPyna6CMAUti11nvzh57hhkmU5Ww9gAOvU38QQQmAx0P81rXK6BAAAjMJbQgAAwHissBjC7XartrbW6TIA47S3t2v27NmSpHXr1sntdjtcEWCWofI3QWAxhGVZSkhIcLoMwGhut5u/E2CI4i0hAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGC/qwFJfX6+ZM2cqIyNDlmVp/fr14WsnTpzQY489pksvvVTnn3++MjIyVFZWpoMHD/Y6Z2VlpSzL6nK0t7dHfUMAACD2RB1Yjh49qokTJ2rZsmVdrn3//ffatWuXFi9erF27dmnt2rX64osvdOutt5523qSkJB06dCjicLvd0ZYHAABi0LBoBxQVFamoqKjbax6PR3V1dRFtL7/8siZNmiSfz6ecnJwe57UsS2lpadGWAwAAhoAB38MSCARkWZZGjBjRa7+2tjaNHj1aWVlZuuWWW7R79+5e+4dCIQWDwYgDAADEpgENLO3t7Xr88cdVUlKipKSkHvtdfPHFqqysVE1NjVatWiW3261rrrlG+/bt63GM1+uVx+MJH9nZ2QNxCwAAwAADFlhOnDih3/3ud+rs7NSrr77aa9/Jkyfrnnvu0cSJE3Xdddfp7bff1rhx4/Tyyy/3OKaiokKBQCB8NDc39/ctAAAAQ0S9h+VMnDhxQnPmzNH+/fu1ZcuWXldXuhMXF6fc3NxeV1hcLpdcLtfZlgoAAAaBfl9hORVW9u3bp82bN2vkyJFRz2HbtpqampSent7f5QEAgEEo6hWWtrY2ffnll+Hz/fv3q6mpScnJycrIyNCdd96pXbt26Z133lFHR4f8fr8kKTk5WcOHD5cklZWVKTMzU16vV5L09NNPa/LkyRo7dqyCwaBeeuklNTU16ZVXXumPewQAAINc1IFl586dmjZtWvi8vLxckjR37lw99dRTqqmpkSRdfvnlEePee+89TZ06VZLk8/kUF/fj4k5ra6vuv/9++f1+eTweXXHFFaqvr9ekSZOiLQ8AAMQgy7Zt2+ki+kMwGJTH41EgEIh6zwwAcx07diz87Kfa2lolJCQ4XBGA/nSmr998lxAAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeFEHlvr6es2cOVMZGRmyLEvr16+PuG7btp566illZGQoISFBU6dO1WeffXbaedesWaPx48fL5XJp/PjxWrduXbSlAQCAGBV1YDl69KgmTpyoZcuWdXv9ueee09KlS7Vs2TI1NjYqLS1NN910k44cOdLjnA0NDSouLlZpaan27Nmj0tJSzZkzRzt27Ii2PAAAEIMs27btPg+2LK1bt06zZs2S9MPqSkZGhhYtWqTHHntMkhQKhZSamqolS5bogQce6Hae4uJiBYNB1dbWhttmzJihCy64QKtWrTqjWoLBoDwejwKBgJKSkvp6SwAMc+zYMRUVFUmSamtrlZCQ4HBFAPrTmb5+9+selv3798vv96uwsDDc5nK5dP3112v79u09jmtoaIgYI0nTp0/vdUwoFFIwGIw4AABAbOrXwOL3+yVJqampEe2pqanhaz2Ni3aM1+uVx+MJH9nZ2WdROQAAMNmAfErIsqyIc9u2u7Sd7ZiKigoFAoHw0dzc3PeCAQCA0Yb152RpaWmSflgxSU9PD7cfPny4ywrKz8f9fDXldGNcLpdcLtdZVgwAAAaDfl1hGTNmjNLS0lRXVxduO378uLZt26b8/Pwex02ZMiVijCRt2rSp1zEAAGDoiHqFpa2tTV9++WX4fP/+/WpqalJycrJycnK0aNEiPfvssxo7dqzGjh2rZ599Vuedd55KSkrCY8rKypSZmSmv1ytJWrhwoQoKCrRkyRLddttt2rBhgzZv3qwPPvigH24RAAAMdlEHlp07d2ratGnh8/LycknS3LlzVVlZqUcffVTHjh3Tgw8+qO+++055eXnatGmTEhMTw2N8Pp/i4n5c3MnPz9fq1av1xBNPaPHixbroootUXV2tvLy8s7k3AAAQI87qOSwm4TksQGziOSxAbHPkOSwAAAADgcACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMaL+ssPgaHAtm21t7c7XQakiN8DvxNzuN1uWZbldBkYQggsQDfa29vDX7gHc8yePdvpEvD/8UWUONd4SwgAABiPFRagG263W7W1tU6XAf3w9lwoFJIkuVwu3oYwhNvtdroEDDEEFqAblmWx3G2Q8847z+kSADiMt4QAAIDxCCwAAMB4BBYAxnv99dd1ww036PXXX3e6FAAOIbAAMFpra6tWrlypzs5OrVy5Uq2trU6XBMABBBYARlu8eLE6OzslSZ2dnfrLX/7icEUAnEBgAWCsnTt36pNPPolo+/jjj7Vz506HKgLgFAILACN1dnbqmWee6fbaM888E151ATA0EFgAGGnHjh0KBoPdXgsGg9qxY8c5rgiAkwgsAIyUl5enpKSkbq95PB7l5eWd44oAOInAAsBIcXFxPW6wffLJJxUXx/++gKGEv3gAxrr66qt16aWXRrRddtlluvLKKx2qCIBTCCwAjPbXv/41vJoSFxfX40ZcALGNwALAaCNGjFBBQYEkqaCgQCNGjHC2IACOILAAMFp7e7s+/fRTSdKnn36q9vZ2hysC4AQCCwCjrVy5Ui0tLZKklpYWVVVVOVwRACcQWAAY68CBA6qqqpJt25Ik27ZVVVWlAwcOOFwZgHONwALASLZt68UXX+yx/VSIATA0EFgAGMnn86mxsVEdHR0R7R0dHWpsbJTP53OoMgBOILAAMFJOTo5yc3MVHx8f0R4fH69JkyYpJyfHocoAOIHAAsBIlmVp4cKFPbZbluVAVQCcQmABYKysrCyVlJSEw4llWSopKVFmZqbDlQE41wgsAIx29913a+TIkZKkUaNGqaSkxOGKADiBwALAaG63W+Xl5UpNTdXDDz8st9vtdEkAHNDvgeXCCy+UZVldjvnz53fbf+vWrd32//zzz/u7NACDVH5+vqqrq5Wfn+90KQAcMqy/J/z5xxA//fRT3XTTTbrrrrt6Hbd3714lJSWFz3/5y1/2d2kAAGCQ6vfA8vOg8be//U0XXXSRrr/++l7HpaSk8KVmAACgWwO6h+X48eN66623dO+99572I4hXXHGF0tPTdeONN+q999477dyhUEjBYDDiAAAAsWlAA8v69evV2tqqefPm9dgnPT1dr732mtasWaO1a9fq17/+tW688UbV19f3OrfX65XH4wkf2dnZ/Vw9AAAwhWUP4BdyTJ8+XcOHD9e//vWvqMbNnDlTlmWppqamxz6hUEihUCh8HgwGlZ2drUAgELEXBgAAmCsYDMrj8Zz29bvf97Cc8t///lebN2/W2rVrox47efJkvfXWW732cblccrlcfS0PAAAMIgP2ltCKFSuUkpKim2++Oeqxu3fvVnp6+gBUBQAABqMBWWHp7OzUihUrNHfuXA0bFvmfqKio0Ndff60333xTkvTCCy/owgsv1CWXXBLepLtmzRqtWbNmIEoDAACD0IAEls2bN8vn8+nee+/tcu3QoUMRXwt//PhxPfLII/r666+VkJCgSy65RBs3btRvf/vbgSgNAAAMQgO66fZcCgQCGjFihJqbm9l0CwDAIHHqQzOtra3yeDw99huwTbfn2pEjRySJjzcDADAIHTlypNfAEjMrLJ2dnTp48KASExNP+5A6AIPLqX+BsYIKxB7btnXkyBFlZGQoLq7nzwLFTGABELvO9DkNAGLXgD7pFgAAoD8QWAAAgPEILACM53K59OSTT/J0a2AIYw8LAAAwHissAADAeAQWAABgPAILAAAwHoEFAAAYj8ACwGivvvqqxowZI7fbrauuukrvv/++0yUBcACBBYCxqqurtWjRIv35z3/W7t27dd1116moqCjiG98BDA18rBmAsfLy8nTllVdq+fLl4bbf/OY3mjVrlrxer4OVATjXWGEBYKTjx4/ro48+UmFhYUR7YWGhtm/f7lBVAJxCYAFgpP/973/q6OhQampqRHtqaqr8fr9DVQFwCoEFgNEsy4o4t227SxuA2EdgAWCkUaNGKT4+vstqyuHDh7usugCIfQQWAEYaPny4rrrqKtXV1UW019XVKT8/36GqADhlmNMFAEBPysvLVVpaqquvvlpTpkzRa6+9Jp/Ppz/+8Y9OlwbgHCOwADBWcXGxWlpa9Mwzz+jQoUOaMGGC3n33XY0ePdrp0gCcYzyHBQAAGI89LAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAY7/8Bwbhswi2LfbcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_df.columns:\n",
    "    sns.boxplot(num_df[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[322, 7, 9, 11, 59, 299, 326, 394]\n"
     ]
    }
   ],
   "source": [
    "def outliers(df, ft):\n",
    "    q1 = df[ft].quantile(0.25)\n",
    "    q3 = df[ft].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    ub = q3 + 1.5 * iqr\n",
    "    lb = q1 - 1.5 * iqr\n",
    "    ls = df.index[(df[ft] > ub) | (df[ft] < lb)]\n",
    "    return ls\n",
    "\n",
    "def replace_outliers_with_mean(df, ft):\n",
    "    q1 = df[ft].quantile(0.25)\n",
    "    q3 = df[ft].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    ub = q3 + 1.5 * iqr\n",
    "    lb = q1 - 1.5 * iqr\n",
    "\n",
    "    outliers_indices = df.index[(df[ft] >= ub) | (df[ft] <= lb)]\n",
    "\n",
    "\n",
    "    df.loc[outliers_indices, ft] = df[ft].mean()\n",
    "\n",
    "    return df\n",
    "\n",
    "index_ls = []\n",
    "\n",
    "for i in num_df.columns:\n",
    "    index_ls.extend(outliers(num_df, i))\n",
    "print(index_ls)\n",
    "\n",
    "for i in num_df.columns:\n",
    "    num_df = replace_outliers_with_mean(num_df, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0+/ZtLVu2TP39/YpEIqqurrbXOJ1OLV++XN3d3ZKkcDis8fHxpDV+v1/BYNBecz+JREKxWCxpAwAAM1fKgdLX16fHH39cTqdTP/zhD9Xe3q4nn3xSkUhEkuTxeJLWezwe+1gkElF2drbmzp37wDX309LSIrfbbW+BQCDVsQEAwDSScqB87WtfU29vry5fvqx/+Id/0JYtW/Rf//Vf9nGHw5G03rKse/bd7fPWNDY2KhqN2tvAwECqYwMAgGkk5UDJzs7WV7/6VZWXl6ulpUWLFy/WT37yE3m9Xkm650rI0NCQfVXF6/VqbGxMw8PDD1xzP06n037l0KcbAACYuR76fVAsy1IikdCCBQvk9XrV2dlpHxsbG1NXV5cqKiokSWVlZcrKykpaMzg4qKtXr9prAAAAMlNZvGfPHoVCIQUCAY2MjKitrU0XL17UuXPn5HA4VFdXp+bmZpWUlKikpETNzc2aM2eONm/eLElyu93atm2b6uvrVVhYqIKCAjU0NKi0tFRVVVWP5AQBAMD0k1KgfPjhh3rhhRc0ODgot9utRYsW6dy5c1q9erUkaffu3YrH49qxY4eGh4e1ZMkSnT9/Xi6Xy76PgwcPKjMzUzU1NYrH46qsrFRra6syMjKm9swAAMC09dDvg5IOvA8KMDPxPijAzPalvA8KAADAo0KgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME5mugeYrSzL0p07d9I9BmCUv/yb4O8DuL+cnBw5HI50j/HIEShpcufOHYVCoXSPARhr/fr16R4BMFJHR4dyc3PTPcYjx0M8AADAOFxBMcDo//mBrMf4VQCyLOnjjz75+rFMaRZcxga+CMfHH+nx3jfTPcaXiv+KBrAey5QystI9BmCI7HQPABjHSvcAacBDPAAAwDgECgAAMA6BAgAAjJNSoLS0tOib3/ymXC6X5s2bp2effVbvvfde0pqtW7fK4XAkbUuXLk1ak0gkVFtbq6KiIuXl5WndunW6cePGw58NAACYEVIKlK6uLu3cuVOXL19WZ2enPvroI1VXV+v27dtJ655++mkNDg7a29mzZ5OO19XVqb29XW1tbbp06ZJGR0e1du1aTUxMPPwZAQCAaS+lV/GcO3cu6faxY8c0b948hcNhfec737H3O51Oeb3e+95HNBrV0aNHdfLkSVVVVUmSTp06pUAgoAsXLmjNmjWpngMAAJhhHuo5KNFoVJJUUFCQtP/ixYuaN2+eFi5cqO3bt2toaMg+Fg6HNT4+rurqanuf3+9XMBhUd3f3fX9OIpFQLBZL2gAAwMw16UCxLEu7du3St771LQWDQXt/KBTSv/3bv+ntt9/Wa6+9pp6eHq1atUqJREKSFIlElJ2drblz5ybdn8fjUSQSue/PamlpkdvttrdAIDDZsQEAwDQw6Tdqe+mll/S73/1Oly5dStq/adMm++tgMKjy8nIVFxfrrbfe0oYNGx54f5ZlPfDDjxobG7Vr1y77diwWI1IAAJjBJnUFpba2Vr/61a/029/+VvPnz//MtT6fT8XFxbp+/bokyev1amxsTMPDw0nrhoaG5PF47nsfTqdT+fn5SRsAAJi5UgoUy7L00ksv6ec//7nefvttLViw4HO/5+bNmxoYGJDP55MklZWVKSsrS52dnfaawcFBXb16VRUVFSmODwAAZqKUHuLZuXOnTp8+rV/+8pdyuVz2c0bcbrdyc3M1OjqqpqYmbdy4UT6fT++//7727NmjoqIi+6PT3W63tm3bpvr6ehUWFqqgoEANDQ0qLS21X9UDAABmt5QC5ciRI5KkFStWJO0/duyYtm7dqoyMDPX19enEiRO6deuWfD6fVq5cqTNnzsjlctnrDx48qMzMTNXU1Cgej6uyslKtra3KyMh4+DMCAADTXkqBYlmf/XmKubm5+s1vfvO595OTk6PXX39dr7/+eio/HgAAzBJ8Fg8AADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwTkqB0tLSom9+85tyuVyaN2+enn32Wb333ntJayzLUlNTk/x+v3Jzc7VixQpdu3YtaU0ikVBtba2KioqUl5endevW6caNGw9/NgAAYEZIKVC6urq0c+dOXb58WZ2dnfroo49UXV2t27dv22v279+vAwcO6PDhw+rp6ZHX69Xq1as1MjJir6mrq1N7e7va2tp06dIljY6Oau3atZqYmJi6MwMAANNWZiqLz507l3T72LFjmjdvnsLhsL7zne/IsiwdOnRIe/fu1YYNGyRJx48fl8fj0enTp/Xiiy8qGo3q6NGjOnnypKqqqiRJp06dUiAQ0IULF7RmzZopOjUAADBdPdRzUKLRqCSpoKBAktTf369IJKLq6mp7jdPp1PLly9Xd3S1JCofDGh8fT1rj9/sVDAbtNXdLJBKKxWJJGwAAmLkmHSiWZWnXrl361re+pWAwKEmKRCKSJI/Hk7TW4/HYxyKRiLKzszV37twHrrlbS0uL3G63vQUCgcmODQAApoFJB8pLL72k3/3ud3rzzTfvOeZwOJJuW5Z1z767fdaaxsZGRaNRexsYGJjs2AAAYBqYVKDU1tbqV7/6lX77299q/vz59n6v1ytJ91wJGRoasq+qeL1ejY2NaXh4+IFr7uZ0OpWfn5+0AQCAmSulQLEsSy+99JJ+/vOf6+2339aCBQuSji9YsEBer1ednZ32vrGxMXV1damiokKSVFZWpqysrKQ1g4ODunr1qr0GAADMbim9imfnzp06ffq0fvnLX8rlctlXStxut3Jzc+VwOFRXV6fm5maVlJSopKREzc3NmjNnjjZv3myv3bZtm+rr61VYWKiCggI1NDSotLTUflUPAACY3VIKlCNHjkiSVqxYkbT/2LFj2rp1qyRp9+7disfj2rFjh4aHh7VkyRKdP39eLpfLXn/w4EFlZmaqpqZG8XhclZWVam1tVUZGxsOdDQAAmBEclmVZ6R4iVbFYTG63W9FodNo+HyUejysUCkmSRr7xgpSRleaJAADGmhiX692TkqSOjg7l5uameaDJSeX/N5/FAwAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBOyoHyzjvv6JlnnpHf75fD4dAvfvGLpONbt26Vw+FI2pYuXZq0JpFIqLa2VkVFRcrLy9O6det048aNhzoRAAAwc6QcKLdv39bixYt1+PDhB655+umnNTg4aG9nz55NOl5XV6f29na1tbXp0qVLGh0d1dq1azUxMZH6GQAAgBknM9VvCIVCCoVCn7nG6XTK6/Xe91g0GtXRo0d18uRJVVVVSZJOnTqlQCCgCxcuaM2aNamOBAAAZphH8hyUixcvat68eVq4cKG2b9+uoaEh+1g4HNb4+Liqq6vtfX6/X8FgUN3d3fe9v0QioVgslrQBAICZK+UrKJ8nFArpueeeU3Fxsfr7+/XKK69o1apVCofDcjqdikQiys7O1ty5c5O+z+PxKBKJ3Pc+W1patG/fvqkeNa0sy/r/NybG0zcIAMB8f/F/Iun/xww25YGyadMm++tgMKjy8nIVFxfrrbfe0oYNGx74fZZlyeFw3PdYY2Ojdu3aZd+OxWIKBAJTN3QaJBIJ+2vX/21L4yQAgOkkkUhozpw56R7jkXvkLzP2+XwqLi7W9evXJUler1djY2MaHh5OWjc0NCSPx3Pf+3A6ncrPz0/aAADAzDXlV1DudvPmTQ0MDMjn80mSysrKlJWVpc7OTtXU1EiSBgcHdfXqVe3fv/9Rj2MMp9Npfz2y+PtSRlYapwEAGG1i3L7a/pf/P2aylANldHRUv//97+3b/f396u3tVUFBgQoKCtTU1KSNGzfK5/Pp/fff1549e1RUVKT169dLktxut7Zt26b6+noVFhaqoKBADQ0NKi0ttV/VMxskPZyVkUWgAAC+kAc9HWKmSTlQrly5opUrV9q3P31uyJYtW3TkyBH19fXpxIkTunXrlnw+n1auXKkzZ87I5XLZ33Pw4EFlZmaqpqZG8XhclZWVam1tVUZGxhScEgAAmO5SDpQVK1Z85jOIf/Ob33zufeTk5Oj111/X66+/nuqPBwAAswCfxQMAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOJnpHgCS4+OPZKV7CMAEliV9/NEnXz+WKTkc6Z0HMITj07+LWYRAMcDjvW+mewQAAIyS8kM877zzjp555hn5/X45HA794he/SDpuWZaamprk9/uVm5urFStW6Nq1a0lrEomEamtrVVRUpLy8PK1bt043btx4qBMBAAAzR8pXUG7fvq3Fixfr7/7u77Rx48Z7ju/fv18HDhxQa2urFi5cqFdffVWrV6/We++9J5fLJUmqq6vTr3/9a7W1tamwsFD19fVau3atwuGwMjIyHv6spoGcnBx1dHSkewzAKHfu3NH69eslSe3t7crJyUnzRIB5ZsvfRcqBEgqFFAqF7nvMsiwdOnRIe/fu1YYNGyRJx48fl8fj0enTp/Xiiy8qGo3q6NGjOnnypKqqqiRJp06dUiAQ0IULF7RmzZqHOJ3pw+FwKDc3N91jAMbKycnhbwSYxab0VTz9/f2KRCKqrq629zmdTi1fvlzd3d2SpHA4rPHx8aQ1fr9fwWDQXnO3RCKhWCyWtAEAgJlrSgMlEolIkjweT9J+j8djH4tEIsrOztbcuXMfuOZuLS0tcrvd9hYIBKZybAAAYJhH8j4ojrteGmhZ1j377vZZaxobGxWNRu1tYGBgymYFAADmmdJA8Xq9knTPlZChoSH7qorX69XY2JiGh4cfuOZuTqdT+fn5SRsAAJi5pjRQFixYIK/Xq87OTnvf2NiYurq6VFFRIUkqKytTVlZW0prBwUFdvXrVXgMAAGa3lF/FMzo6qt///vf27f7+fvX29qqgoEBf+cpXVFdXp+bmZpWUlKikpETNzc2aM2eONm/eLElyu93atm2b6uvrVVhYqIKCAjU0NKi0tNR+VQ8AAJjdUg6UK1euaOXKlfbtXbt2SZK2bNmi1tZW7d69W/F4XDt27NDw8LCWLFmi8+fP2++BIkkHDx5UZmamampqFI/HVVlZqdbW1lnzHigAAOCzOSzLmnYfAxOLxeR2uxWNRnk+CjCDxONx+32WOjo6eB8UYIZJ5f83n2YMAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAONkpnsAIN0sy9KdO3fSPQakpN8DvxOz5OTkyOFwpHsMzCIECma9O3fuKBQKpXsM3GX9+vXpHgF/oaOjQ7m5uekeA7MID/EAAADjcAUFs15OTo46OjrSPQb0ycNtiURCkuR0OnlIwSA5OTnpHgGzDIGCWc/hcHDp2iBz5sxJ9wgADMBDPAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4Ux4oTU1NcjgcSZvX67WPW5alpqYm+f1+5ebmasWKFbp27dpUjwEAAKaxR3IF5etf/7oGBwftra+vzz62f/9+HThwQIcPH1ZPT4+8Xq9Wr16tkZGRRzEKAACYhh5JoGRmZsrr9drbE088IemTqyeHDh3S3r17tWHDBgWDQR0/flx//vOfdfr06UcxCgAAmIYeSaBcv35dfr9fCxYs0Pe//339z//8jySpv79fkUhE1dXV9lqn06nly5eru7v7gfeXSCQUi8WSNgAAMHNN+acZL1myRCdOnNDChQv14Ycf6tVXX1VFRYWuXbumSCQiSfJ4PEnf4/F49Ic//OGB99nS0qJ9+/bds59QAQBg+vj0/7ZlWZ+71mF9kVUP4fbt2/rrv/5r7d69W0uXLtXf/u3f6oMPPpDP57PXbN++XQMDAzp37tx97yORSCiRSNi3//d//1dPPvnkoxwbAAA8IgMDA5o/f/5nrpnyKyh3y8vLU2lpqa5fv65nn31WkhSJRJICZWho6J6rKn/J6XTK6XTatx9//HENDAzI5XLJ4XA8stkBfPlisZgCgYAGBgaUn5+f7nEATCHLsjQyMiK/3/+5ax95oCQSCf33f/+3vv3tb2vBggXyer3q7OzUU089JUkaGxtTV1eXfvzjH3/h+3zsscc+t7wATG/5+fkECjADud3uL7RuygOloaFBzzzzjL7yla9oaGhIr776qmKxmLZs2SKHw6G6ujo1NzerpKREJSUlam5u1pw5c7R58+apHgUAAExTUx4oN27c0A9+8AP96U9/0hNPPKGlS5fq8uXLKi4uliTt3r1b8XhcO3bs0PDwsJYsWaLz58/L5XJN9SgAAGCaeuRPkgWAVCQSCbW0tKixsTHpuWcAZhcCBQAAGIcPCwQAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAWCUn/70p1qwYIFycnJUVlam//iP/0j3SADSgEABYIwzZ86orq5Oe/fu1X/+53/q29/+tkKhkP74xz+mezQAXzJeZgzAGEuWLNE3vvENHTlyxN73N3/zN3r22WfV0tKSxskAfNm4ggLACGNjYwqHw6qurk7aX11dre7u7jRNBSBdCBQARvjTn/6kiYmJez7Z3OPxKBKJpGkqAOlCoAAwisPhSLptWdY9+wDMfAQKACMUFRUpIyPjnqslQ0ND91xVATDzESgAjJCdna2ysjJ1dnYm7e/s7FRFRUWapgKQLpnpHgAAPrVr1y698MILKi8v17Jly/TGG2/oj3/8o374wx+mezQAXzICBYAxNm3apJs3b+qf/umfNDg4qGAwqLNnz6q4uDjdowH4kvE+KAAAwDg8BwUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGCc/weod0aKrcsGpAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_df.columns:\n",
    "    sns.boxplot(num_df[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADK15\\AppData\\Local\\Temp\\ipykernel_27584\\2615055963.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(num_df)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(num_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pt = PowerTransformer(method='yeo-johnson')\n",
    "# num_df = pd.DataFrame(pt.fit_transform(num_df),columns=num_df.columns)\n",
    "# sns.distplot(num_df)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.calibration import LabelEncoder\n",
    "\n",
    "\n",
    "le = LabelEncoder()\n",
    "obj_df = obj_df.apply(le.fit_transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.50000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.50000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.60000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>15.56809</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.60000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.60000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.40000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  weight  acceleration  model year  origin\n",
       "0    18.0          8         307.0    3504      12.00000          70       1\n",
       "1    15.0          8         350.0    3693      11.50000          70       1\n",
       "2    18.0          8         318.0    3436      11.00000          70       1\n",
       "3    16.0          8         304.0    3433      12.00000          70       1\n",
       "4    17.0          8         302.0    3449      10.50000          70       1\n",
       "..    ...        ...           ...     ...           ...         ...     ...\n",
       "393  27.0          4         140.0    2790      15.60000          82       1\n",
       "394  44.0          4          97.0    2130      15.56809          82       2\n",
       "395  32.0          4         135.0    2295      11.60000          82       1\n",
       "396  28.0          4         120.0    2625      18.60000          82       1\n",
       "397  31.0          4         119.0    2720      19.40000          82       1\n",
       "\n",
       "[398 rows x 7 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = pd.concat([num_df, obj_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.50000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.50000</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.60000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>15.56809</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.60000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.60000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.40000</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  weight  acceleration  model year  origin  \\\n",
       "0    18.0          8         307.0    3504      12.00000          70       1   \n",
       "1    15.0          8         350.0    3693      11.50000          70       1   \n",
       "2    18.0          8         318.0    3436      11.00000          70       1   \n",
       "3    16.0          8         304.0    3433      12.00000          70       1   \n",
       "4    17.0          8         302.0    3449      10.50000          70       1   \n",
       "..    ...        ...           ...     ...           ...         ...     ...   \n",
       "393  27.0          4         140.0    2790      15.60000          82       1   \n",
       "394  44.0          4          97.0    2130      15.56809          82       2   \n",
       "395  32.0          4         135.0    2295      11.60000          82       1   \n",
       "396  28.0          4         120.0    2625      18.60000          82       1   \n",
       "397  31.0          4         119.0    2720      19.40000          82       1   \n",
       "\n",
       "     horsepower  car name  \n",
       "0            15        49  \n",
       "1            33        36  \n",
       "2            27       231  \n",
       "3            27        14  \n",
       "4            22       161  \n",
       "..          ...       ...  \n",
       "393          80       153  \n",
       "394          51       301  \n",
       "395          78       119  \n",
       "396          73       159  \n",
       "397          76        69  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8443102305226398\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "X = result_df.iloc[:, 1:]\n",
    "y = result_df.iloc[:, :1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "clf = linear_model.Lasso(alpha=0.1)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)    \n",
    "print(r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8472273911114008\n"
     ]
    }
   ],
   "source": [
    "ridge = linear_model.Ridge(alpha=1.0)\n",
    "ridge.fit(X_train, y_train)\n",
    "y_pred = ridge.predict(X_test)\n",
    "print(r2_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8448705666238476\n"
     ]
    }
   ],
   "source": [
    "elastic = linear_model.ElasticNet(alpha=0.1)\n",
    "elastic.fit(X_train, y_train)\n",
    "y_pred = elastic.predict(X_test)\n",
    "print(r2_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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