Prediction using Supervised ML/Supervised ML .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TO EXPLORE SUPERVISED MACHINE LEARNING (LINEAR REGRESSION) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Importing important libraries---\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is successfully imported\n"
     ]
    },
    {
     "data": {
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       "<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",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>7.8</td>\n",
       "      <td>86</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\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##imprting Dataset-\n",
    "path =  \"http://bit.ly/w-data\"\n",
    "Data = pd.read_csv(path)\n",
    "print(\"Data is successfully imported\")\n",
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
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       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
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       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
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       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
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      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Now print the first 5 records...\n",
    "\n",
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <td>20</td>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
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       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>7.8</td>\n",
       "      <td>86</td>\n",
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      "text/plain": [
       "    Hours  Scores\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Now print the last 5 records...\n",
    "Data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <td>count</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here we use describe() method so that we can able to see percentiles,mean,std,max,count of the given dataset.\n",
    "Data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "Hours     25 non-null float64\n",
      "Scores    25 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#Let's print the full summary of the dataframe .\n",
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "##importing libraries for plotting Graphs\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "plt.boxplot(Data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "##ploting Scatter plot----\n",
    "plt.xlabel('Hours',fontsize=15)\n",
    "plt.ylabel('Scores',fontsize=15)\n",
    "plt.title('Hours studied vs Score', fontsize=10)\n",
    "plt.scatter(Data.Hours,Data.Scores,color='blue',marker='*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This \"SCATTER PLOT\" indicates positive linear relationship as much as hours You study is a chance of high scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5],\n",
       "       [5.1],\n",
       "       [3.2],\n",
       "       [8.5],\n",
       "       [3.5],\n",
       "       [1.5],\n",
       "       [9.2],\n",
       "       [5.5],\n",
       "       [8.3],\n",
       "       [2.7],\n",
       "       [7.7],\n",
       "       [5.9],\n",
       "       [4.5],\n",
       "       [3.3],\n",
       "       [1.1],\n",
       "       [8.9],\n",
       "       [2.5],\n",
       "       [1.9],\n",
       "       [6.1],\n",
       "       [7.4],\n",
       "       [2.7],\n",
       "       [4.8],\n",
       "       [3.8],\n",
       "       [6.9],\n",
       "       [7.8]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Data.iloc[:,:-1].values\n",
    "Y = Data.iloc[:,1].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
       "       24, 67, 69, 30, 54, 35, 76, 86], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Data and splitting into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,random_state = 0,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train.shape = (20, 1)\n",
      "Y train.shape = (20,)\n",
      "X test.shape  = (5, 1)\n",
      "Y test.shape  = (5,)\n"
     ]
    }
   ],
   "source": [
    "## We have Splitted Our Data Using 80:20 RULe(PARETO)\n",
    "print(\"X train.shape =\", X_train.shape)\n",
    "print(\"Y train.shape =\", Y_train.shape)\n",
    "print(\"X test.shape  =\", X_test.shape)\n",
    "print(\"Y test.shape  =\", Y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linreg=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training our algorithm is finished\n"
     ]
    }
   ],
   "source": [
    "##Fitting Training Data\n",
    "linreg.fit(X_train,Y_train)\n",
    "print(\"Training our algorithm is finished\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B0 = 2.018160041434683 \n",
      "B1 = [9.91065648]\n"
     ]
    }
   ],
   "source": [
    "print(\"B0 =\",linreg.intercept_,\"\\nB1 =\",linreg.coef_)## β0 is Intercept & Slope of the line is β1.,\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "##plotting the REGRESSION LINE---\n",
    "Y0 = linreg.intercept_ + linreg.coef_*X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "##plotting on train data\n",
    "plt.scatter(X_train,Y_train,color='green',marker='+')\n",
    "plt.plot(X_train,Y0,color='orange')\n",
    "plt.xlabel(\"Hours\",fontsize=15)\n",
    "plt.ylabel(\"Scores\",fontsize=15)\n",
    "plt.title(\"Regression line(Train set)\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.88414476 33.73226078 75.357018   26.79480124 60.49103328]\n"
     ]
    }
   ],
   "source": [
    "Y_pred=linreg.predict(X_test)##predicting the Scores for test data\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 27, 69, 30, 62], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now print the Y_test.\n",
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": [
    "#plotting line on test data\n",
    "plt.plot(X_test,Y_pred,color='red')\n",
    "plt.scatter(X_test,Y_test,color='black',marker='+')\n",
    "plt.xlabel(\"Hours\",fontsize=15)\n",
    "plt.ylabel(\"Scores\",fontsize=15)\n",
    "plt.title(\"Regression line(Test set)\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Actual vs Predicted Scores.¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual     Result\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test1 = list(Y_test)\n",
    "prediction=list(Y_pred)\n",
    "df_compare = pd.DataFrame({ 'Actual':Y_test1,'Result':prediction})\n",
    "df_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACCURACY OF THE MODEL¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9454906892105356"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.r2_score(Y_test,Y_pred)##Goodness of fit Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above 94% percentage indicates that above fitted Model is a GOOD MODEL. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error      =  21.5987693072174\n",
      "Root Mean Squared Error =  4.6474476121003665\n",
      "Mean Absolute Error     =  4.6474476121003665\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(Y_test,Y_pred)\n",
    "root_E = np.sqrt(metrics.mean_squared_error(Y_test,Y_pred))\n",
    "Abs_E = np.sqrt(metrics.mean_squared_error(Y_test,Y_pred))\n",
    "print(\"Mean Squared Error      = \",MSE)\n",
    "print(\"Root Mean Squared Error = \",root_E)\n",
    "print(\"Mean Absolute Error     = \",Abs_E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the score¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted score for a student studying 9.25 hours : [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "Prediction_score = linreg.predict([[9.25]])\n",
    "print(\"predicted score for a student studying 9.25 hours :\",Prediction_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONCLUSION: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above result we can say that if a studied for 9.25 then student will secured 93.69 MARKS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completed task#2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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