GRIP PROGRAM-TASK 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# P.VIJAY SRINIVASAN \n",
    "\n",
    "# THE SPARKS FOUNDATION\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRIP-TASK 1 \n",
    "\n",
    "\n",
    "# PREDICTION USING SUPERVISED LEARNING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TASK-1:PREDICTION USING SUPERVISED MACHINE LEARNING\n"
     ]
    }
   ],
   "source": [
    "print(\"TASK-1:PREDICTION USING SUPERVISED MACHINE LEARNING\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 9,
   "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",
       "      <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=r'https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv'\n",
    "df=pd.read_csv(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This data is so user friendly as it does not consists of any null values and so no pre-processing needs to be done\n",
    "# It consists of number of hours studied by students and marks scored based by the student based  -->\n",
    "# on the total hours of studying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOW LET'S PLAY WITH THE DATA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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": [
    "df['Hours'].plot(linestyle='-',linewidth=0.6,label='number of hours studied',color='red')\n",
    "df['Scores'].plot(linestyle='dashed',linewidth=0.6,label='marks scored',color='blue')\n",
    "plt.xlabel('hours')\n",
    "plt.ylabel('marks')\n",
    "plt.legend(loc=1)\n",
    "plt.title(\"Relationship between hours and scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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.scatter(df['Hours'],df['Scores'],s=10,color='blue')\n",
    "plt.xlabel('hours')\n",
    "plt.ylabel('scores')\n",
    "plt.title('Hours spent to study and its result')\n",
    "plt.show()\n",
    "# here from this we can come to a conclusion that except some of the data that is not propotional to hours spent and marks scored\n",
    "# But most of the data falls under direct relationship of hours spent and marks scored\n",
    "# AS hours spent increases the marks that the student will score will increase,there fore it is directly propotional to each\n",
    "# other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours\n",
       "0     2.5\n",
       "1     5.1\n",
       "2     3.2\n",
       "3     8.5\n",
       "4     3.5\n",
       "5     1.5\n",
       "6     9.2\n",
       "7     5.5\n",
       "8     8.3\n",
       "9     2.7\n",
       "10    7.7\n",
       "11    5.9\n",
       "12    4.5\n",
       "13    3.3\n",
       "14    1.1\n",
       "15    8.9\n",
       "16    2.5\n",
       "17    1.9\n",
       "18    6.1\n",
       "19    7.4\n",
       "20    2.7\n",
       "21    4.8\n",
       "22    3.8\n",
       "23    6.9\n",
       "24    7.8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "X=df.drop('Scores',axis=1)\n",
    "y=df['Scores']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)\n",
    "X\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now let's use machine learning to train the machine to predict the scores based in number of hours\n",
    "# now we need to split this data into training data which is fed to the machine \n",
    "# testing data is used as a new data from which the machine will predict scores based on it's previous training experience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression(fit_intercept=True)\n",
    "model.fit(X_train,y_train)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "# now let's plot the linear line or slope \n",
    "slope=model.coef_*X+model.intercept_\n",
    "slope\n",
    "plt.scatter(X,y,s=10,color='black')\n",
    "plt.plot(X,slope,linestyle='solid',color='blue')\n",
    "plt.xlabel('hours')\n",
    "plt.ylabel('marks scored')\n",
    "plt.title('Linear relationship between hours and marks scored')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     20\n",
       "2     27\n",
       "19    69\n",
       "16    30\n",
       "11    62\n",
       "22    35\n",
       "17    24\n",
       "24    86\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(X_test)\n",
    "y_pred\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean absolute error 4.419727808027652\n",
      "mean squared error 22.96509721270043\n",
      "root mean squared error 4.792191274636315\n",
      "r2_score 0.9568211104435257\n"
     ]
    },
    {
     "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>Predicted scores</th>\n",
       "      <th>Actual scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.053665</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.694229</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>74.806209</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>26.842232</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60.123359</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>39.567369</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20.969092</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>78.721636</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predicted scores  Actual scores\n",
       "5          17.053665             20\n",
       "2          33.694229             27\n",
       "19         74.806209             69\n",
       "16         26.842232             30\n",
       "11         60.123359             62\n",
       "22         39.567369             35\n",
       "17         20.969092             24\n",
       "24         78.721636             86"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's see how well our trained model has performed\n",
    "# model's performance can be measured by using mean absolute error,r2_score,mean squared error,root mean squared error\n",
    "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error\n",
    "df1=pd.DataFrame({'Predicted scores':y_pred,'Actual scores':y_test})\n",
    "print('mean absolute error',mean_absolute_error(y_test,y_pred))\n",
    "print('mean squared error',mean_squared_error(y_test,y_pred))\n",
    "print('root mean squared error',np.sqrt(mean_squared_error(y_test,y_pred)))\n",
    "print('r2_score',r2_score(y_test,y_pred))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "if a student studies for 9.25 hrs/day his estimated score will be [92.91505723]\n"
     ]
    }
   ],
   "source": [
    "# now let's predict what will be the mark scored by a student if he studied 9.25 hours/day\n",
    "time=np.array(9.25)\n",
    "time=time.reshape(-1,1)\n",
    "predicted_score=model.predict(time)\n",
    "print('if a student studies for 9.25 hrs/day his estimated score will be {}'.format(predicted_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9484997422695115\n",
      "The accuracy of the model is 0.9568211104435257\n"
     ]
    }
   ],
   "source": [
    "training_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(training_score)\n",
    "print('The accuracy of the model is {}'.format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By using this linear regressor we were able to predict the score when a student studies for 9.25 hrs/day and the PREDICTED\n",
    "# SCORE IS 92.91%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THANK YOU"
   ]
  }
 ],
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