To Explore Supervised Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this regression task we will predict the percentage of\n",
    "marks that a student is expected to score based upon the\n",
    "number of hours they studied. This is a simple linear\n",
    "regression task as it involves just two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "\n",
    "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#importing dataset from url\n",
    "\n",
    "dataset = pd.read_csv('http://bit.ly/w-data')\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seprating target variable\n",
    "X = np.array(dataset['Hours']).reshape(-1, 1) \n",
    "y = np.array(dataset['Scores']).reshape(-1, 1) #target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualizing sample data from dataset\n",
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23aa6f16208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar graph representation of given data\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x=dataset['Hours'],y=dataset['Scores'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction\n",
    "lin_reg_pred = lin_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.844722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.745575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.500624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.786400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.588106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>39.710582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>20.821393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.844722\n",
       "1      27  33.745575\n",
       "2      69  75.500624\n",
       "3      30  26.786400\n",
       "4      62  60.588106\n",
       "5      35  39.710582\n",
       "6      24  20.821393"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Actual': y_test.reshape(-1,), 'Predicted': lin_reg_pred.reshape(-1,)})  \n",
    "df1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "#Visualising the Training set results\n",
    "\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, lin_reg.predict(X_train), color = 'blue')\n",
    "plt.title('On Training Set')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Visualising the Test set results\n",
    "\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_train, lin_reg.predict(X_train), color = 'blue')\n",
    "plt.title('On Test Set')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted score if a student study for 9.25 hrs = [[93.89272889]]\n"
     ]
    }
   ],
   "source": [
    "print('predicted score if a student study for 9.25 hrs = {}'.format(lin_reg.predict([[9.25]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.130879918502486\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test,lin_reg_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9367661043365055"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-square value\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,lin_reg_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DecisionTree regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=0, splitter='best')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dec_reg = DecisionTreeRegressor(random_state = 0)\n",
    "dec_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction\n",
    "dec_reg_pred = dec_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20       17.0\n",
       "1      27       42.0\n",
       "2      69       85.0\n",
       "3      30       21.0\n",
       "4      62       67.0\n",
       "5      35       30.0\n",
       "6      24       21.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Actual': y_test.reshape(-1,), 'Predicted': dec_reg_pred.reshape(-1,)})  \n",
    "df2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted score if a student study for 9.25 hrs = [88.]\n"
     ]
    }
   ],
   "source": [
    "print('predicted score if a student study for 9.25 hrs = {}'.format(dec_reg.predict([[9.25]])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 8.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test,dec_red_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7201066260472202"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared value\n",
    "r2_score(y_test,dec_red_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest regresssion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chandu\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
       "                      random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "ran_reg = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
    "ran_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction\n",
    "ran_reg_pred = ran_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>18.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>34.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>85.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>25.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>61.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>31.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>18.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  18.200000\n",
       "1      27  34.300000\n",
       "2      69  85.300000\n",
       "3      30  25.033333\n",
       "4      62  61.800000\n",
       "5      35  31.100000\n",
       "6      24  18.600000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'Actual': y_test.reshape(-1,), 'Predicted': ran_reg_pred.reshape(-1,)})  \n",
    "df3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted score if a student study for 9.25 hrs = [88.1]\n"
     ]
    }
   ],
   "source": [
    "print('predicted score if a student study for 9.25 hrs = {}'.format(ran_reg.predict([[9.25]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.6952380952380945\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test,ran_reg_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8261561027897663"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,ran_reg_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVR model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For svr model we need to do feature scaling.In real-world dataset contains features that vary in magnitudes, units, and range. I would suggest performing normalization when the scale of a feature is irrelevant or misleading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "X_train1 = sc_X.fit_transform(X_train)\n",
    "y_train1 = sc_y.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chandu\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_reg = SVR(kernel = 'rbf')\n",
    "svr_reg.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction\n",
    "svr_reg_pred = sc_y.inverse_transform(svr_reg.predict(sc_X.transform(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>23.045773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>31.192401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>80.290900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.245404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>63.068588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>36.578359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>23.606623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  23.045773\n",
       "1      27  31.192401\n",
       "2      69  80.290900\n",
       "3      30  26.245404\n",
       "4      62  63.068588\n",
       "5      35  36.578359\n",
       "6      24  23.606623"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'Actual': y_test.reshape(-1,), 'Predicted': svr_reg_pred.reshape(-1,)})  \n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.617713449568749\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test,svr_reg_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted score if a student study for 9.25 hrs = [84.93264486]\n"
     ]
    }
   ],
   "source": [
    "print('predicted score if a student study for 9.25 hrs = {}'.format(sc_y.inverse_transform(svr_reg.predict(sc_X.transform([[9.25]])))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9234859375446093"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,svr_reg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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