Lasso Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7daac54d-f53a-47d8-9bff-5aa1d075af19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "52527cef-79ff-41c3-ba07-28cf5c91c22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"AQI_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "64ef6ff4-6758-4168-86f1-490bd9ea02e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed1bf403-88db-4c1c-9b90-510ff54a7ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cf9f0384-3e27-46cb-8319-6c3da9dea2f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05850b7d-4ec4-43e6-b51f-5a948f64a554",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.iloc[:,:-1] ## independent features\n",
    "Y=data.iloc[:,-1]  ## dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa69aa54-952f-4f7b-8044-cb2567212468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T      0\n",
       "TM     0\n",
       "Tm     0\n",
       "SLP    0\n",
       "H      0\n",
       "VV     0\n",
       "V      0\n",
       "VM     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7d4ac994-d660-45ee-8969-3eaac8a28d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ec52b986-747b-4415-88f3-9b2e91717452",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=ExtraTreesRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f43dba94-adb7-43df-9cb5-7cac955cc9fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5d284e24-da24-4d11-9acf-811dd6f415aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14976965 0.11272625 0.24349627 0.13062245 0.08813186 0.17879822\n",
      " 0.05287674 0.04357855]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fc7ca5a6-4974-4c91-ad31-30603095e388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Zaft27dXvl6/fn3ccsst8eqrr1bW1dfXH/nIAIARr093VJqamipLY2NjlEqlXuvq6+ujo6MjSqVSbNy4Mc4888wYP358fPnLX4533303fve738Upp5wSDQ0Ncc0118S+ffv6+7wAgGGgT3dUqrFixYr42c9+FhMmTIhFixbFokWLolwux7333ht79+6Nyy67LO6888648cYbD7p/T09P9PT0VP7e3d090EMGAJIY8BfT/vjHP47zzz8/zjzzzGhtbY0nn3wy7r777jjzzDPjggsuiCuuuCKeeOKJQ+6/cuXKaGxsrCzNzc0DPWQAIIkBD5VZs2ZVvp4yZUpMmDAhTjzxxF7r3n333UPuv3z58ujq6qos27ZtG9DxAgB5DPiPfsaMGVP5ulQq9fr7R+sOHDhwyP3L5XKUy+UBGx8AkJffowIApCVUAIC0hAoAkFapKIqi1oOoRnd394fv/vneAzGqPKHWwwGAYaNz1cIBO/ZH1++urq5oaGg47P3cUQEA0hIqAEBaQgUASEuoAABpCRUAIC2hAgCkJVQAgLSECgCQllABANISKgBAWkIFAEhLqAAAadXVegB99bcfza/qQ40AgKHHHRUAIC2hAgCkJVQAgLSECgCQllABANISKgBAWkIFAEhLqAAAaQkVACAtoQIApCVUAIC0hAoAkJZQAQDSEioAQFpCBQBIS6gAAGnV1XoA1SqKIiIiuru7azwSAOBwfXTd/ug6friGXKjs3LkzIiKam5trPBIAoFp79uyJxsbGw95+yIXK0UcfHRERb7zxRlUnSv/p7u6O5ubm2LZtWzQ0NNR6OCOSOag9c1B75qD2qpmDoihiz549MW3atKqeY8iFyqhRH76sprGx0X/MGmtoaDAHNWYOas8c1J45qL3DnYO+3GDwYloAIC2hAgCkNeRCpVwuR1tbW5TL5VoPZcQyB7VnDmrPHNSeOai9wZiDUlHt+4QAAAbJkLujAgCMHEIFAEhLqAAAaQkVACCtmofKXXfdFccff3yMGzcuZs+eHc8///wnbr9hw4aYOXNmjBs3Lk4//fT47W9/2+vxoijilltuialTp8b48eNj3rx58dprrw3kKQx5/T0HS5YsiVKp1GtZsGDBQJ7CkFfNHLz00ktx+eWXx/HHHx+lUinuuOOOIz4m/T8HK1as+Nj3wcyZMwfwDIa+aubg5z//eVxwwQVx1FFHxVFHHRXz5s372PauB9Xr7znol+tBUUP3339/MXbs2GLNmjXFSy+9VCxdurSYNGlS8c477xx0+02bNhWjR48ubrvttuLll18ufvjDHxZjxowp/vrXv1a2WbVqVdHY2Fg89NBDxQsvvFBccsklxQknnFD8+9//HqzTGlIGYg4WL15cLFiwoNi+fXtl+ec//zlYpzTkVDsHzz//fLFs2bLivvvuK5qamorbb7/9iI850g3EHLS1tRWnnnpqr++D9957b4DPZOiqdg6uueaa4q677io2b95cvPLKK8WSJUuKxsbG4s0336xs43pQnYGYg/64HtQ0VM4555ziu9/9buXv+/fvL6ZNm1asXLnyoNsvWrSoWLhwYa91s2fPLr7zne8URVEUBw4cKJqamoqf/vSnlcd3795dlMvl4r777huAMxj6+nsOiuLD/5iXXnrpgIx3OKp2Dv5fM2bMOOhF8kiOORINxBy0tbUVZ5xxRj+Ocng70v+z//3vf4uJEycW69atK4rC9aAv+nsOiqJ/rgc1+9HP+++/H3/+859j3rx5lXWjRo2KefPmxTPPPHPQfZ555ple20dEzJ8/v7L91q1bY8eOHb22aWxsjNmzZx/ymCPZQMzBRzo6OuJTn/pUfO5zn4sbbrih8qnX9NaXOajFMYezgfz3eu2112LatGlx4oknxrXXXhtvvPHGkQ53WOqPOdi3b1988MEHlQ+udT2ozkDMwUeO9HpQs1D5xz/+Efv3748pU6b0Wj9lypTYsWPHQffZsWPHJ27/0Z/VHHMkG4g5iIhYsGBB3HPPPfH444/HT37yk3jyySfjoosuiv379/f/SQxxfZmDWhxzOBuof6/Zs2dHe3t7PPLII3H33XfH1q1b44ILLog9e/Yc6ZCHnf6YgxtvvDGmTZtWudC6HlRnIOYgon+uB0Pu05PJ7xvf+Ebl69NPPz1mzZoVJ510UnR0dMTcuXNrODIYPBdddFHl61mzZsXs2bNjxowZ8cADD0Rra2sNRzb8rFq1Ku6///7o6OiIcePG1Xo4I9Kh5qA/rgc1u6MyefLkGD16dLzzzju91r/zzjvR1NR00H2ampo+cfuP/qzmmCPZQMzBwZx44okxefLkeP3114980MNMX+agFscczgbr32vSpElx8skn+z44iCOZg9WrV8eqVavi97//fcyaNauy3vWgOgMxBwfTl+tBzUJl7Nix8YUvfCEef/zxyroDBw7E448/Hueee+5B9zn33HN7bR8R8eijj1a2P+GEE6KpqanXNt3d3fHcc88d8pgj2UDMwcG8+eabsXPnzpg6dWr/DHwY6csc1OKYw9lg/Xvt3bs3tmzZ4vvgIPo6B7fddlvceuut8cgjj8TZZ5/d6zHXg+oMxBwcTJ+uB0f0UtwjdP/99xflcrlob28vXn755eLb3/52MWnSpGLHjh1FURTFN7/5zeIHP/hBZftNmzYVdXV1xerVq4tXXnmlaGtrO+jbkydNmlT8+te/Ll588cXi0ksv9Xa0T9Dfc7Bnz55i2bJlxTPPPFNs3bq1eOyxx4qzzjqr+OxnP1v85z//qck5ZlftHPT09BSbN28uNm/eXEydOrVYtmxZsXnz5uK111477GPS20DMwfe///2io6Oj2Lp1a7Fp06Zi3rx5xeTJk4t333130M9vKKh2DlatWlWMHTu2ePDBB3u99XXPnj29tnE9OHz9PQf9dT2oaagURVHceeedxfTp04uxY8cW55xzTvHss89WHpszZ06xePHiXts/8MADxcknn1yMHTu2OPXUU4vf/OY3vR4/cOBAcfPNNxdTpkwpyuVyMXfu3OLVV18djFMZsvpzDvbt21dceOGFxbHHHluMGTOmmDFjRrF06VIXyP+hmjnYunVrEREfW+bMmXPYx+Tj+nsOrrrqqmLq1KnF2LFji+OOO6646qqritdff30Qz2joqWYOZsyYcdA5aGtrq2zjelC9/pyD/roelIqiKA7//gsAwOCp+a/QBwA4FKECAKQlVACAtIQKAJCWUAEA0hIqAEBaQgUASEuoAABpCRUAIC2hAgCkJVQAgLSECgCQ1v8BV1UcRjW0IxwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760b28b1-7db0-4c1c-90d2-b0857bf120f2",
   "metadata": {},
   "source": [
    "### Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f1f9da6e-6e6a-4c51-b0fc-db2235f3f088",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kanch\\AppData\\Local\\Temp\\ipykernel_23636\\3269117342.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(Y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PM 2.5', ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec614d26-ffe8-4584-ae2b-13a2dd0069e9",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c6c2453b-0d13-46b8-9449-7ced47566da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7de45702-d98b-4972-b471-155034060be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b99138f2-7bb3-447c-a1fd-d19524adbf63",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8c6c6e5d-2d71-44e6-b360-472546f6b625",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7cef044c-cb5e-4106-8d3d-eec963059f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0aa9ab23-ca72-4300-b095-6d6bc74d8b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.56692199e+00,  2.88757870e+00, -5.61453346e+00, -1.51390043e-03,\n",
       "       -8.14580326e-01, -3.53314179e+01, -1.59900171e+00,  3.03600119e-01])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "55d8d068-e8ab-44ce-ad23-3f0a39c657f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "321.06121352786647"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1d8d52f4-5f90-46ed-ad5f-6778883502d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination R^2 <-- on train set: 0.5324832185062109\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficient of determination R^2 <-- on train set: {}\".format(regressor.score(X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1430856c-cbd2-4e9b-97ee-3d8add7d9a29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination R^2 <-- on train set: 0.5296122198998006\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficient of determination R^2 <-- on train set: {}\".format(regressor.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3ca1c1-1d54-4806-9c21-cc9c509a0ea6",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1713a27d-62b4-42da-a300-e389c550fb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(regressor,X,Y,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "04fdfbb5-4918-4e13-8672-3d05cda19f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47341130136999776"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "95240963-dbb0-42b7-999c-b9877af08f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9eec17b1-0d7a-49d9-8cd6-5e125da8a5c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab2b42f4-88ab-4055-897a-c06bfd43bd37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 42.530406555081576\n",
      "MSE: 3454.8267264416263\n",
      "RMSE: 58.777774085462156\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d29dcb6-01f6-4c16-bf18-0e51b54eac13",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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