Polynomial Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "simple linear regression y= mx+c"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "multiple linear regression y=m1 * x1 + m2 *x2 + c1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "polynomial linear regression y= m1 * x^1 + m2* x^2 + m3 * x^3 + ... + m(n) * x^(n) +c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "in polynomial linear regression we can use only one feature for our prediction so we to choosea good feature with good resemblance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'iris' from 'sklearn.datasets' (c:\\users\\harsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\datasets\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-9594fca4b57c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'iris' from 'sklearn.datasets' (c:\\users\\harsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\datasets\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_df=pd.read_csv('C:\\\\Users\\\\harsh\\\\Desktop\\\\boston_house_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "      <td>0.00632</td>\n",
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       "      <td>7.07</td>\n",
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       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
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       "      <td>17.8</td>\n",
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       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
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       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
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       "      <td>0.458</td>\n",
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       "      <td>3</td>\n",
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       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
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       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
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      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_df=boston_df.RM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_df=boston_df.MEDV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\harsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x_df,y_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x_df,y_df,random_state=0,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220    6.951\n",
       "71     5.961\n",
       "240    6.897\n",
       "6      6.012\n",
       "417    5.304\n",
       "       ...  \n",
       "323    5.708\n",
       "192    7.178\n",
       "117    6.021\n",
       "47     6.030\n",
       "172    5.572\n",
       "Name: RM, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_feature=PolynomialFeatures(degree=2)\n",
    "\n",
    "x_train=np.array(x_train).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_x=poly_feature.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.      ,  6.951   , 48.316401],\n",
       "       [ 1.      ,  5.961   , 35.533521],\n",
       "       [ 1.      ,  6.897   , 47.568609],\n",
       "       ...,\n",
       "       [ 1.      ,  6.021   , 36.252441],\n",
       "       [ 1.      ,  6.03    , 36.3609  ],\n",
       "       [ 1.      ,  5.572   , 31.047184]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linreg=LinearRegression()\n",
    "\n",
    "linreg.fit(poly_x,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=np.array(x_test).reshape(-1,1)\n",
    "poly_xt=poly_feature.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.65341999, 20.65130216, 22.01210356, 15.40968359, 20.78420634,\n",
       "       17.8674847 , 18.32836006, 18.75892052, 14.33314132, 22.30494953,\n",
       "       18.68227876, 24.7943528 , 21.72450125, 13.98692364, 40.2008255 ,\n",
       "       29.21303535, 20.86793715, 32.44056149, 23.65775743, 17.9690908 ,\n",
       "       22.4999794 , 18.16313557, 19.9359494 , 23.03109942, 16.96760859,\n",
       "       15.37829483, 18.73795678, 17.11881373, 44.75869991, 19.66574295,\n",
       "       20.39753078, 19.58211647, 20.73421365, 19.6886577 , 22.4068264 ,\n",
       "       17.67435336, 18.89276116, 18.13047473, 21.78696619, 20.21242878,\n",
       "       19.9359494 , 17.57053189, 20.06945591, 15.72367701, 23.59811723,\n",
       "       22.45333889, 17.59482733, 19.95157924, 14.21265627, 25.54356466,\n",
       "       20.13279458, 16.90160457, 18.85029599, 48.22444889, 17.39684022,\n",
       "       18.22224769, 17.10182881, 18.28177459, 17.39684022, 18.70311961,\n",
       "       18.18935642, 19.82711407, 28.48502791, 23.71758198, 17.14153198,\n",
       "       23.75756743, 24.77331544, 22.04842255, 26.94385224, 18.23544005,\n",
       "       21.18219366, 21.24243619, 21.27697315, 27.19858313, 25.79521093,\n",
       "       14.38631976, 48.00009051, 21.59149273, 22.41611865, 17.6192047 ,\n",
       "       24.26429855, 22.76353207, 16.5723308 , 48.35566514, 57.50735208,\n",
       "       21.46839096, 20.22042037, 17.88643599, 23.30268144, 19.35719353,\n",
       "       18.21565919, 24.50169374, 19.78848754, 27.16370115, 19.20248964,\n",
       "       19.78848754, 14.53143324, 22.10305464, 23.44982325, 17.30285381,\n",
       "       22.03025281, 22.95424194])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction=linreg.predict(poly_xt)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "continuous is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-b072f58a82db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0macc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\harsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\harsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[1;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m     \u001b[1;31m# Compute accuracy for each possible representation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 202\u001b[1;33m     \u001b[0my_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    203\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'multilabel'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\harsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m     98\u001b[0m     \u001b[1;31m# No metrics support \"multiclass-multioutput\" format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"binary\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"multiclass\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"multilabel-indicator\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{0} is not supported\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"binary\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"multiclass\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: continuous is not supported"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "acc=accuracy_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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