Student Marks Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load DataSet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"student_info.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>study_hours</th>\n",
       "      <th>student_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.83</td>\n",
       "      <td>78.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.56</td>\n",
       "      <td>76.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>78.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.67</td>\n",
       "      <td>71.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.67</td>\n",
       "      <td>84.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   study_hours  student_marks\n",
       "0         6.83          78.50\n",
       "1         6.56          76.74\n",
       "2          NaN          78.68\n",
       "3         5.67          71.82\n",
       "4         8.67          84.19"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 2)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "study_hours      5\n",
       "student_marks    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   study_hours    195 non-null    float64\n",
      " 1   student_marks  200 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>study_hours</th>\n",
       "      <th>student_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>200.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.995949</td>\n",
       "      <td>77.93375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.253060</td>\n",
       "      <td>4.92570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>68.57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.775000</td>\n",
       "      <td>73.38500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.120000</td>\n",
       "      <td>77.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.085000</td>\n",
       "      <td>82.32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.990000</td>\n",
       "      <td>86.99000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       study_hours  student_marks\n",
       "count   195.000000      200.00000\n",
       "mean      6.995949       77.93375\n",
       "std       1.253060        4.92570\n",
       "min       5.010000       68.57000\n",
       "25%       5.775000       73.38500\n",
       "50%       7.120000       77.71000\n",
       "75%       8.085000       82.32000\n",
       "max       8.990000       86.99000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'student_marks')"
      ]
     },
     "execution_count": 102,
     "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": [
    "plt.scatter(x = df.study_hours, y = df.student_marks)\n",
    "plt.xlabel('study_hours')\n",
    "plt.ylabel('student_marks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the Data for machine Learning algorithms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "study_hours      5\n",
       "student_marks    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Clearing\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "study_hours       6.995949\n",
       "student_marks    77.933750\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "study_hours      0\n",
       "student_marks    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>study_hours</th>\n",
       "      <th>student_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.830000</td>\n",
       "      <td>78.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.560000</td>\n",
       "      <td>76.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.995949</td>\n",
       "      <td>78.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.670000</td>\n",
       "      <td>71.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.670000</td>\n",
       "      <td>84.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   study_hours  student_marks\n",
       "0     6.830000          78.50\n",
       "1     6.560000          76.74\n",
       "2     6.995949          78.68\n",
       "3     5.670000          71.82\n",
       "4     8.670000          84.19"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.drop('student_marks', axis=1)\n",
    "y = df2.student_marks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X  (200, 1)\n",
      "shape of y  (200,)\n"
     ]
    }
   ],
   "source": [
    "print('shape of X ',X.shape)\n",
    "print('shape of y ',y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X_train (160, 1)\n",
      "shape of y_train (160,)\n",
      "shape of X_test (40, 1)\n",
      "shape of y_test (40,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state = 51)\n",
    "\n",
    "print('shape of X_train', X_train.shape)\n",
    "print('shape of y_train', y_train.shape)\n",
    "print('shape of X_test', X_test.shape)\n",
    "print('shape of y_test', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select a model and train it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = m*x+c\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.93571802])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_   # M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.44735503694244"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_   # C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.28190000000001"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = m*x+c\n",
    "m = 3.93\n",
    "c = 50.44\n",
    "y = m*6.830000+c\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.19"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[4]])[0].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([83.11381458, 78.9025963 , 84.57003024, 85.82946001, 84.72745896,\n",
       "       80.75238377, 72.84159055, 71.66087515, 73.23516235, 71.66087515,\n",
       "       73.47130543, 76.38373677, 73.23516235, 73.58937697, 82.95638585,\n",
       "       70.40144538, 73.23516235, 78.74516758, 75.55723598, 82.68088559,\n",
       "       76.65923703, 70.48015974, 74.77009238, 77.98143645, 85.59331693,\n",
       "       82.56281405, 76.42309395, 85.0423164 , 78.39095296, 81.38209865,\n",
       "       81.73631327, 83.15317176, 82.20859943, 81.10659839, 73.58937697,\n",
       "       71.1492318 , 71.89701823, 81.53952737, 72.60544747, 71.93637541])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148    82.02\n",
       "104    77.55\n",
       "4      84.19\n",
       "7      85.46\n",
       "192    84.03\n",
       "160    80.81\n",
       "118    73.61\n",
       "58     70.90\n",
       "190    73.14\n",
       "174    73.02\n",
       "23     75.02\n",
       "10     75.37\n",
       "115    74.44\n",
       "86     73.40\n",
       "67     81.70\n",
       "68     69.27\n",
       "177    73.64\n",
       "171    77.63\n",
       "128    77.01\n",
       "14     83.08\n",
       "82     76.63\n",
       "50     72.22\n",
       "45     72.96\n",
       "31     76.14\n",
       "176    85.96\n",
       "21     83.36\n",
       "198    78.05\n",
       "89     84.60\n",
       "35     76.76\n",
       "36     81.24\n",
       "113    80.86\n",
       "121    82.69\n",
       "99     82.30\n",
       "162    79.17\n",
       "79     73.34\n",
       "131    71.86\n",
       "65     70.06\n",
       "13     80.76\n",
       "85     72.87\n",
       "42     71.10\n",
       "Name: student_marks, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Student_hours</th>\n",
       "      <th>Student_marks_Original</th>\n",
       "      <th>Student_marks_predi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.300000</td>\n",
       "      <td>82.02</td>\n",
       "      <td>83.113815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.230000</td>\n",
       "      <td>77.55</td>\n",
       "      <td>78.902596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.670000</td>\n",
       "      <td>84.19</td>\n",
       "      <td>84.570030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.990000</td>\n",
       "      <td>85.46</td>\n",
       "      <td>85.829460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.710000</td>\n",
       "      <td>84.03</td>\n",
       "      <td>84.727459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>80.81</td>\n",
       "      <td>80.752384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.690000</td>\n",
       "      <td>73.61</td>\n",
       "      <td>72.841591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.390000</td>\n",
       "      <td>70.90</td>\n",
       "      <td>71.660875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.790000</td>\n",
       "      <td>73.14</td>\n",
       "      <td>73.235162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.390000</td>\n",
       "      <td>73.02</td>\n",
       "      <td>71.660875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.850000</td>\n",
       "      <td>75.02</td>\n",
       "      <td>73.471305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.590000</td>\n",
       "      <td>75.37</td>\n",
       "      <td>76.383737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.790000</td>\n",
       "      <td>74.44</td>\n",
       "      <td>73.235162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.880000</td>\n",
       "      <td>73.40</td>\n",
       "      <td>73.589377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260000</td>\n",
       "      <td>81.70</td>\n",
       "      <td>82.956386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.070000</td>\n",
       "      <td>69.27</td>\n",
       "      <td>70.401445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.790000</td>\n",
       "      <td>73.64</td>\n",
       "      <td>73.235162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.190000</td>\n",
       "      <td>77.63</td>\n",
       "      <td>78.745168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.380000</td>\n",
       "      <td>77.01</td>\n",
       "      <td>75.557236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.190000</td>\n",
       "      <td>83.08</td>\n",
       "      <td>82.680886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.660000</td>\n",
       "      <td>76.63</td>\n",
       "      <td>76.659237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.090000</td>\n",
       "      <td>72.22</td>\n",
       "      <td>70.480160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.180000</td>\n",
       "      <td>72.96</td>\n",
       "      <td>74.770092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.995949</td>\n",
       "      <td>76.14</td>\n",
       "      <td>77.981436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.930000</td>\n",
       "      <td>85.96</td>\n",
       "      <td>85.593317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8.160000</td>\n",
       "      <td>83.36</td>\n",
       "      <td>82.562814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.600000</td>\n",
       "      <td>78.05</td>\n",
       "      <td>76.423094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8.790000</td>\n",
       "      <td>84.60</td>\n",
       "      <td>85.042316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>76.76</td>\n",
       "      <td>78.390953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.860000</td>\n",
       "      <td>81.24</td>\n",
       "      <td>81.382099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.950000</td>\n",
       "      <td>80.86</td>\n",
       "      <td>81.736313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8.310000</td>\n",
       "      <td>82.69</td>\n",
       "      <td>83.153172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8.070000</td>\n",
       "      <td>82.30</td>\n",
       "      <td>82.208599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7.790000</td>\n",
       "      <td>79.17</td>\n",
       "      <td>81.106598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>5.880000</td>\n",
       "      <td>73.34</td>\n",
       "      <td>73.589377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.260000</td>\n",
       "      <td>71.86</td>\n",
       "      <td>71.149232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.450000</td>\n",
       "      <td>70.06</td>\n",
       "      <td>71.897018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>80.76</td>\n",
       "      <td>81.539527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5.630000</td>\n",
       "      <td>72.87</td>\n",
       "      <td>72.605447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.460000</td>\n",
       "      <td>71.10</td>\n",
       "      <td>71.936375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Student_hours  Student_marks_Original  Student_marks_predi\n",
       "0        8.300000                   82.02            83.113815\n",
       "1        7.230000                   77.55            78.902596\n",
       "2        8.670000                   84.19            84.570030\n",
       "3        8.990000                   85.46            85.829460\n",
       "4        8.710000                   84.03            84.727459\n",
       "5        7.700000                   80.81            80.752384\n",
       "6        5.690000                   73.61            72.841591\n",
       "7        5.390000                   70.90            71.660875\n",
       "8        5.790000                   73.14            73.235162\n",
       "9        5.390000                   73.02            71.660875\n",
       "10       5.850000                   75.02            73.471305\n",
       "11       6.590000                   75.37            76.383737\n",
       "12       5.790000                   74.44            73.235162\n",
       "13       5.880000                   73.40            73.589377\n",
       "14       8.260000                   81.70            82.956386\n",
       "15       5.070000                   69.27            70.401445\n",
       "16       5.790000                   73.64            73.235162\n",
       "17       7.190000                   77.63            78.745168\n",
       "18       6.380000                   77.01            75.557236\n",
       "19       8.190000                   83.08            82.680886\n",
       "20       6.660000                   76.63            76.659237\n",
       "21       5.090000                   72.22            70.480160\n",
       "22       6.180000                   72.96            74.770092\n",
       "23       6.995949                   76.14            77.981436\n",
       "24       8.930000                   85.96            85.593317\n",
       "25       8.160000                   83.36            82.562814\n",
       "26       6.600000                   78.05            76.423094\n",
       "27       8.790000                   84.60            85.042316\n",
       "28       7.100000                   76.76            78.390953\n",
       "29       7.860000                   81.24            81.382099\n",
       "30       7.950000                   80.86            81.736313\n",
       "31       8.310000                   82.69            83.153172\n",
       "32       8.070000                   82.30            82.208599\n",
       "33       7.790000                   79.17            81.106598\n",
       "34       5.880000                   73.34            73.589377\n",
       "35       5.260000                   71.86            71.149232\n",
       "36       5.450000                   70.06            71.897018\n",
       "37       7.900000                   80.76            81.539527\n",
       "38       5.630000                   72.87            72.605447\n",
       "39       5.460000                   71.10            71.936375"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.c_[X_test, y_test, y_pred], columns = [\"Student_hours\",\"Student_marks_Original\",\"Student_marks_predi\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9514124242154464"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x218431ac388>"
      ]
     },
     "execution_count": 126,
     "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": [
    "plt.scatter(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x218437b2348>]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f/iG57dAh6NEjnHikJGnhEZEococ+faAxYabIv/wFRo0KLyaJNC08IlJK7rkHjjmmJclPmhQkfiV56SBNgSASFa+/Dqecktz21ltw3HFFD0VrwJYX9ehFomDEiOQkX1sb9OJDSvIzFqyhfm8jDtTvbWTGgjXUrqpv91yJJiV6kTAtWhSMf3/++WB7xIggwU+cGFpI6ZYK1BqwpU2lG5EwvP02HH98ctvrr0P//uHEkyDTUoFaA7Z0qUcvUmyTJiUn+Z/9LOjFRyDJQ+alArUGbOlSohcplqeeCso0CxcG28cdF0xd8OUvhxtXimljh1HRI3mSWa0BW9pUuhEptKYm6JmyXMPGjTAk92X8ikFLBZYfJXqRQrrhBviP/2jZvukm+Pa3w4snS1oqsLwo0YsUwvr1cOaZyW3NzdBN6+5I8alGL5JP7kEdPjHJr1wZtCvJS0iU6EXy5Yc/DKYuiPv854MEf8454cUkgko3Ip1XXw8DBya3NTZCr17hxCOSQj16kc4YNCg5yS9ZEvTileQlQtSjl5IRqYm25s6FT3+6ZfvCC2H58nBiEWmHEr2UhPhEW/E5WOITbQHFTfZ790Lfvsltu3e3bhOJEJVupCREYqKtiy5KTuj/9V9BmUZJXiJOPXopCaFOtPXEE3DxxS3bAwfC9u2Ff1+RPFGil5IwoLKC+jRJvaATbR082Pqm6vbtrUfYiEScSjdSEoo+0daXvpSc5G+7LSjT5Jjka1fVM2r2MgZPX8yo2cu0eIeEot3Fwc1sGPBQQtMQ4CbgAiD+v6wS2OvuI9KcvwV4CzgMNGdavDaRFgeXdIoy6mbVqtYPOB0+nPwgVJZSbyADGOBAVdijhqTstLU4eLulG3ffAIyIvVA3oB5Y6O5HZ2oysx8A+9p4mY+4+xs5RS2SoqATbR0+DN1T/jusXdt6vpocpLuBHO9WhTZqSLqkXLspY4CN7r413mBmBlwBzM1nYCJFc8styUn+q18NyjSdSPLQ/o1iLc8nxZLrzdjJtE7oHwZec/eXM5zjwBIzc+Aed5+T7iAzmwpMBTjttNNyDEukAzZvbj0n/MGDreeO76BMN5ATaXk+KYase/Rm1hOYAMxL2XUlbffmR7n7OcA44DozG53uIHef4+7V7l7dPyJLqkmZcofKyuQk/+STQXuekjykv4GcSsvzSTHkUroZBzzn7q/FG8ysOzCJ5Ju1Sdx9Z+zz68BC4NyOhSqSB/feG9xY3Re7pTRhQpDgP/zhvL9VzcgqZk0aTlUsmVvKfi3PJ8WSS+kmXc/9IuBv7r4j3Qlm1gc4xt3fin19CXBLhyKVkhb6PDVvvNF68e39+5MX6S6AxBvIoV8D6bKySvRm1hu4GEhdxbhVzd7MBgD3uvt44BRgYXC/lu7Ab9z9D50NWkpL6PPUVFcHi3/E/e53MGlS4d83hZbnk7Bklejd/QBwYpr2KWnadgLjY19vAs7uXIhS6tqap6agie/RR+Hyy1u2zzwzGDIp0sVoCgQpuKLPU/POO3Dcccltr74Kp5xSkLdTSUaiTlMgSMFlGllSkBEnV1yRnOR/+tPgZmsBk/yMBWuo39uI01KW0lQHEiVK9FJwhZynJj6XzKSr7ggW5Z4XG/177LFw5Ah85Sudfo+2RGL6ZJF2qHQjBRcvY+S7vFG7qp5/n7+atd+/LKl9ae2fuXji/+7Ua2cr1OmTRbKkRC9FUYgRJwe+9nXW/vnho9t3XXAFd4z+HFUvHuLiiXl9q4xCmT5ZJEdK9FJ6NmyA008nYcVWhkxbxJFjgvJQMXvT08YOazVDpR6EkqhRopfS4d5quuAJn/shL5z6vqS2YvamC1WWEsknJXopDT/+MVx/fcv2Zz9L7ddn8/KCNRByb1oPQknUKdFLtO3cCVUpSfTAAaiooCa2WejetMbJS6lTopfoGjoUXnmlZfuxx2DcuKRDCt2bDn36BpE80Dh6iZ6HHgrGxMeT/KhRQX0+JckXg8bJSzlQj16iY9++YJ74RG++Cf36hRMPGicv5UE9eomGSy9NTvK//GXQiw8xyUORp28QKRAlegnX8uVBmebxx4Ptk08OEvzVV4cbV0whp28QKRaVbiQcBw9Cr17JbVu3QsTWC9Y4eSkHSvRSfNdcA/fc07L9/e/DjBnhxdMOjZOXUqdEL8Xzwgtwdso6NIcPt3raVUTyS4leCu/IEeiWXOfmhRdg+PCsX0IPLYl0nLpSUljf+15ykr/22uBma45JXot7iHScevRSGFu3wqBByW0HD0LPnjm/VGhrzoqUCfXoJf9OOik5yS9fHvTiO5DkQQ8tiXRWu4nezIaZ2eqEj/1mdr2Z3Wxm9Qnt4zOcf6mZbTCzV8xsev6/BYGWJfUGT1/MqNnLwilr3H9/MCb+zTeD7fHjgwR/4YWdelk9tCTSOe2Wbtx9AzACwMy6AfXAQuDzwI/c/Y5M58aO/wlwMbADeNbMHnH39XmIXWJCn3jrzTeDXnyiffvgXe/Ky8trcQ+Rzsm1dDMG2OjuW7M8/lzgFXff5O6HgN8CRVrkresIdeKtCy5ITvIPPxz04vOU5CH4ZTVr0nCqKiswoKqyglmThqs+L5KlXG/GTgbmJmz/i5l9DqgD/s3d96QcXwVsT9jeAZyX7oXNbCowFeC0iD0dGXWh1LAfeww+9rGW7fe9L1jir0D00JJIx2XdozeznsAEYF6s6W7gfxGUdXYBP0h3Wpo2T/f67j7H3avdvbp///7ZhiUUuYZ94EBQh09M8jt3FjTJi0jn5FK6GQc85+6vAbj7a+5+2N2PAD8nKNOk2gG8J2F7ILCzo8FKekWbeOszn4E+fVq277wzKNOcemp+30dE8iqX0s2VJJRtzOxUd98V2/w4sDbNOc8CQ81sMMFN3MnApzsYq2RQ8Im3nnkGzkuouB1zDDQ3Bz17EYm8rBK9mfUmGDnz5YTm28xsBEEpZkt8n5kNAO519/Hu3mxm/wI8DnQD7nP3dXmMv0vKNB1A3mvYzc3Qo0dy24YNQT1eREpGVone3Q8AJ6a0XZXh2J3A+ITtx4DHOhGjJCjaUMoZM2D27Jbtb3wDbr01f68vIkWjKRBKTMGnA3jpJRiWUttvaoLu+lERKVWaAqHEFGwopXsw+Vhikn/66aBdSV6kpCnRl5iCDKW8667gBuuRI8H25MlBgj8v7SMPIlJi1FUrMXmdDuDVV1sPjXznHejdu5NRikiUqEdfYvI2HcD735+c5B99NOjFK8mLlB316EtQp4ZSzp8P//zPLdvnngsrVuQnMBGJJCX6rmL/fjjhhOS2hobWs06KSNlRoo+ottZIzXn91Msug8WLW7Z/8Qv4whcK/B2ISFQo0UdQWw9FAdk/MPXkk/BP/9Sy3a8fvPGGpi4Q6WKU6COovfnl231g6tAhOPbY5BfdvLn1Gq4i0iVo1E0EtfVQVHsPTG361JSkJL/+2mnBaBoleZEuSz36CBpQWUF9moQefygq3b5Rf38VzBiS0Db4G4/QvVs3bl9Vr0U7RLowJfoIau+hqMR95kfYfNuEpPPHff5OXjw5SPlNR5ybH1mnRC/ShSnRR1B788vXbd3N3BXb+dJf5zH9T788et5vzh7LNy/911avt7exqShxi0g0KdF3Qs7DHHOQ6aGo2lX1PLVsNRv/83NJ7Y88vZFvLnwxL+8tIuVFib6DijYvfIr//U9nUfPW7qPbV07+Hn9979lULd9C39492HOgde+9b+8erdo6opC/2ESkcDTqpoPaGwKZi9pV9YyavYzB0xczavYyalfVtz7oV78CM06KJfknB41k0I2P8tf3ng0Eo25mXn4mPbolj5Hv0c2YefmZOceULsYZC9ZQv7cRp+UXW9pYRSRS1KPvoHzNC9/uXwa7d8OJSYt7Mfz6h3jr2D5JbQMqKwq6dmzBFzwRkYJRou+g9oZAZqvNBPq1K+HPf27ZMXcutcM+TPOCNZBhRE5B1o6lgAueiEjBqXTTQdPGDqOiR7ekto7MC58uUY7etJKnZoxpSfJDhgQPPU2enL9pinNUkAVPRKQo2u3Rm9kw4KGEpiHATUAVcDlwCNgIfN7d96Y5fwvwFnAYaHb36s6HHb58lUkS/zI4tukgG374ieQD6uthwIBW713sckleFzwRkaIyd8/+YLNuQD1wHjAMWObuzWZ2K4C735jmnC1Atbu/ke37VFdXe11dXdZxlbJ4jf67C2/jE+uWH21f8/WZDP/BzeEFloZG3YhEl5mtzNSRzrVGPwbY6O5bga0J7U8Dn+xgfF1azeFd1Hx3XFJb7crt1JwzMKSIMgvjLwkR6bxcE/1kYG6a9i+QXN5J5MASM3PgHnefk+N7lqfDh6F7yuV/8UU4/XRqwolIRMpU1jdjzawnMAGYl9L+LaAZ+HWGU0e5+znAOOA6Mxud4fWnmlmdmdU1NDRkG1a7shqjXmz//u/JSf7rXw9utp5+engxiUjZyqVHPw54zt1fizeY2dXAZcAYz1Dsd/edsc+vm9lC4FzgyTTHzQHmQFCjzyGujMJ6ejWjV16BoUOT2w4dgh75eXJVRCSdXIZXXklC2cbMLgVuBCa4+4F0J5hZHzM7Pv41cAmwtuPh5iafT692ijv06pWc5P/nf4J2JXkRKbCsEr2Z9QYuBhYkNN8FHA8sNbPVZvaz2LEDzOyx2DGnAH8xs+eBZ4DF7v6HvEXfjkg85HP33XDMMXDwYLD9yU8GCf6CC4oXg4h0aVmVbmI99hNT2v4hw7E7gfGxrzcBZ3cyxg7r6NOreRlG+Npr8O53J7e9/Tb06ZP+eBGRAinrJ2M78vRqXibvOuus5CS/aFHQi1eSF5EQlHWi78h0AZ2q6y9cCGawJrjhy8iRQYKfMKHt80RECqjsJzXL9SGfDtX1334bjj8+ue3116F//6zfV0SkUMq6R98ROU/eVVOTnOTvuSfoxSvJi0hEKNGnyLqu/5e/BGWaRYuC7eOPhyNHYOrUIkUqIpKdsi/d5KrdWSmbmqBnz+STNm4MphIWEYkgJfo0Mtb1r78efvzjlu2ZM+Hmm4sWl4hIRyjRZ2PdOvjAB5LbmpuhW7f0x4uIRIgSfVvcg6daEz33XDBsMgPN2S4iUaObsZnccUdykv/CF4LE306S7/TDViIieaYefaodO+A970lua2wMJiVLkdp7P3CoOfNC3+rVi0hI1KNPdNppyUl+yZKWmSdTpOu97znQlPZlizqJmohICiV6gN/8JhgTv317sP2RjwQJ/uKLM56SbqqETNqbRE1EpJC6dunmnXfguOOS2/bsgcrKdk/Ntpfe3iRqIiKF1nV79N/+dnKSf/DBoBefRZKHzL30yooeOU2iJiJSaF2vR79mTTCNcNw11wSLg+Ro2thhScsUQtB7v3nCmUrsIhIpXSfRNzUFQyPXrWtpe+MNOPHEzOe0od2pEkREIqJrJPq774Zrr23ZXrQoL3PE5zoFsohIGMo70W/enDzZ2MSJLYuDiIh0EeWZ6I8cgbFj4YknWtq2bWv9IJSISBdQfqNu5s0LJhuLJ/n77w9G0xQhydeuqmfU7GUMnr6YUbOXaeoDEYmEdhO9mQ0zs9UJH/vN7Hoz62dmS83s5djnvhnOvzp2zMtmdnX+v4UE558PV1wRfP3BDwY3YKdMKehbxmmeGxGJqnYTvbtvcPcR7j4C+EfgALAQmA780d2HAn+MbScxs37ATOA84FxgZqZfCHkRX9Jv/Xp45hnoXrzKVKcWFRcRKaBcSzdjgI3uvhWYCDwQa38AqElz/Fhgqbvvdvc9wFLg0o4G266lS4MyzRlnFOwtMunQouIiIkWQa6KfDMyNfX2Ku+8CiH0+Oc3xVcD2hO0dsbZWzGyqmdWZWV1DQ0OOYYUv50XFRUSKJOtEb2Y9gQnAvBxeP904Rk93oLvPcfdqd6/u379/Dm8RDVkvKi4iUmS59OjHAc+5+2ux7dfM7FSA2OfX05yzA0gc7jIQ2NmRQKOuZmQVsyYN1zw3IhI5udytvJKWsg3AI8DVwOzY50Vpznkc+H7CDdhLgBkdiLMk6ElZEYmirHr0ZtYbuBhYkNA8G7jYzF6O7ZsdO7bazO4FcPfdwHeAZ2Mft8TaRESkSMw9bck8VNXV1V5XVxd2GCIiJWxgzIEAAAUbSURBVMPMVrp7dbp95TkFAq3Xc9XMkiLSVZVloo8/pRp/gCn+lCqgZC8iXU75zXWDnlIVEUlUloleT6mKiLQoy0Svp1RFRFqUZaLXU6oiIi3K8mas1nMVEWlRloke9JSqiEhcWZZuRESkhRK9iEiZU6IXESlzSvQiImVOiV5EpMxFcvZKM2sAtnbg1JOAN/IcTj4ortxENS6IbmyKKzflGNd73T3t8nyRTPQdZWZ1mabpDJPiyk1U44Loxqa4ctPV4lLpRkSkzCnRi4iUuXJL9HPCDiADxZWbqMYF0Y1NceWmS8VVVjV6ERFprdx69CIikkKJXkSkzJVkojezLWa2xsxWm1ldmv1mZnea2Stm9oKZnRORuC40s32x/avN7KYixVVpZvPN7G9m9qKZXZCyP6zr1V5cYV2vYQnvudrM9pvZ9SnHFP2aZRlXWNfsBjNbZ2ZrzWyumfVK2X+smT0Uu14rzGxQROKaYmYNCdfri0WK62uxmNal/hvG9uf358vdS+4D2AKc1Mb+8cDvAQPOB1ZEJK4LgUdDuF4PAF+Mfd0TqIzI9WovrlCuV0oM3YBXCR5GCf2aZRFX0a8ZUAVsBipi2w8DU1KOuRb4WezrycBDEYlrCnBXka/XB4C1QG+CqeKfAIYW8uerJHv0WZgI/MoDTwOVZnZq2EGFwczeBYwGfgHg7ofcfW/KYUW/XlnGFQVjgI3unvqkdtg/Y5niCkt3oMLMuhMksJ0p+ycS/GIHmA+MMTOLQFxhOAN42t0PuHsz8Cfg4ynH5PXnq1QTvQNLzGylmU1Ns78K2J6wvSPWFnZcABeY2fNm9nszO7MIMQ0BGoD7zWyVmd1rZn1SjgnjemUTFxT/eqWaDMxN0x7Wz1hcprigyNfM3euBO4BtwC5gn7svSTns6PWKJbd9wIkRiAvgE7HyyHwze08hY4pZC4w2sxPNrDdB7z31ffP681WqiX6Uu58DjAOuM7PRKfvT9RSKMY60vbieI/hT+2zgP4HaIsTUHTgHuNvdRwLvANNTjgnjemUTVxjX6ygz6wlMAOal252mrShjlduJq+jXzMz6EvRABwMDgD5m9tnUw9KcWtDrlWVc/w8Y5O5nEZRQHqDA3P1F4FZgKfAH4HmgOeWwvF6vkkz07r4z9vl1YCFwbsohO0j+DTmQIvzJ1l5c7r7f3d+Off0Y0MPMTipwWDuAHe6+IrY9nyDBph5T7OvVblwhXa9E44Dn3P21NPtC+RmLyRhXSNfsImCzuze4exOwAPhQyjFHr1esjHICsDvsuNz9TXc/GNv8OfCPBY4p/r6/cPdz3H00wXV4OeWQvP58lVyiN7M+ZnZ8/GvgEoI/hRI9Anwuduf6fII/2XaFHZeZvTtelzSzcwmu/5uFjMvdXwW2m9mwWNMYYH3KYUW/XtnEFcb1SnElmcsjRb9m2cQV0jXbBpxvZr1j7z0GeDHlmEeAq2NffxJY5rG7jmHGlVL3npC6v1DM7OTY59OASbT+98zvz1cx7zbn44Ogtvt87GMd8K1Y+zXANbGvDfgJsBFYA1RHJK5/ie17Hnga+FCRrtkIoA54geBP+b5hX68s4wrlesXeuzdBgjwhoS0K16y9uML6Gfs28DeCzs1/AccCtwATYvt7EZSaXgGeAYZEJK5ZCddrOXB6keL6M0HH5nlgTKF/vjQFgohImSu50o2IiORGiV5EpMwp0YuIlDklehGRMqdELyJS5pToRUTKnBK9iEiZ+/+jYoG3QxGr4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test)\n",
    "plt.plot(X_train, lr.predict(X_train), color =\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Present your solution "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save ML Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Students_mark_predictor_model.pkl']"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(lr, \"Students_mark_predictor_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"Students_mark_predictor_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.12594512018406"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5]])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}