Task01 Student Percentage Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task #1 : Prediction using Supervised ML "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, our task is to predict the percentage of a student based on the number of study hours. Yes, it is a simple linear regression as it involves just 2 variables.\n",
    "\n",
    "Here we go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Necessary Packages for the task**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading the Dataset and having its overview**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#url = 'http://bit.ly/w-data'\n",
    "#stu = pd.read_csv(url)\n",
    "\n",
    "file = 'score_student.csv'\n",
    "stu = pd.read_csv(file)\n",
    "stu.head()"
   ]
  },
  {
   "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": [
    "stu.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking for Duplicate and Null values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now lets have a look on how does the two variable vary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stu.plot(kind = 'scatter', x='Hours', y='Scores', style= 'o', figsize=(12,6),color = 'r', s = 50)\n",
    "\n",
    "plt.title(\"A scatter plot of number of hours studied and percentage exam scores.\",fontsize = 16)\n",
    "\n",
    "plt.xlabel('Hours Studied',fontsize = 14)\n",
    "plt.ylabel('Percentage Scores',fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the above plot we can notice that there is a positive and linear relation between Hours studied and Percentage Scores and we can verify it by correlation method in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Preparing the Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we'll create two arrays having values of hours in \"X\" and values of Percentage Scores in \"Y\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = stu.iloc[:,0].values.reshape(-1,1)\n",
    "Y = stu.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's Split our data for training and testing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2,random_state= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Training the algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the model with data\n"
     ]
    }
   ],
   "source": [
    "Lr  = LinearRegression()\n",
    "Lr.fit(X_train,Y_train)\n",
    "print(\"Training the model with data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of coeff : 9.910656480642237\n",
      "The value of intercept : 2.018160041434683\n"
     ]
    }
   ],
   "source": [
    "print(\"The value of coeff :\",Lr.coef_[0])\n",
    "print(\"The value of intercept :\",Lr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting the Regression Line**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_line = Lr.coef_ * X + Lr.intercept_\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(X,Y,color = 'r')\n",
    "\n",
    "plt.title(\"A scatter plot of number of hours studied and percentage exam scores.\",fontsize = 16)\n",
    "plt.xlabel('Hours Studied',fontsize = 13)\n",
    "plt.ylabel('Percentage Scores',fontsize = 13)\n",
    "plt.plot(X,reg_line)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Making Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yhat = Lr.predict(X_test)\n",
    "Yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's compare Predicted values with actual ones.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 Values</th>\n",
       "      <th>Predicted Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual Values  Predicted Values\n",
       "0             20             16.88\n",
       "1             27             33.73\n",
       "2             69             75.36\n",
       "3             30             26.79\n",
       "4             62             60.49"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = pd.DataFrame({'Actual Values':Y_test, 'Predicted Values':Yhat.round(2)})\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**R-squared Value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value of training dataset : 0.95155\n",
      "R-squared value of testing dataset : 0.94549\n"
     ]
    }
   ],
   "source": [
    "print(\"R-squared value of training dataset :\",Lr.score(X_train,Y_train).round(5))\n",
    "print(\"R-squared value of testing dataset :\",Lr.score(X_test,Y_test).round(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's plot a bar chart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u8H0XiQh23333FrXmG34lSVI2DL7vPi39TA2/kiRJ7Wj27NlEBEuXLm123u9+97ts2LCh1du67rrrmDx58lbjli9fTt++fdm8efNW44cMGcIjjzzS4HqWL1/OwIEDW11HZ2L4lSRJWYqo7qNSM2fO5JBDDmHWrFnNzru94bchNTU17L333vz617/eMm7p0qWsW7eOkSNHVnVbnZHhV5IkqZ2sX7+eBx54gGuvvXar8Ltp0ybOP/98Bg0axODBg7nyyiv53ve+x5/+9CfGjh3L2LFjAdhll122LHPLLbdwxhlnAPCzn/2Mj3zkIwwdOpQjjzySF198sck6xo8fv9X2Z82axfjx41m+fDmjR49m2LBhDBs2jAcffHCbZeu3Jo8bN4758+cDcNddd3HQQQcxbNgwTjnlFNavXw/AhRdeyIABAxg8eDDnn39+y3ZalXm1B0mSpHZy2223ceyxx/KhD32Inj178thjjzFs2DCmTZvGc889x+OPP07Xrl15+eWX6dmzJ9/5zneYN28ee+yxR5PrPeSQQ3j44YeJCKZPn87ll1/Ot7/97UbnP/XUUxk6dChXXnklXbt25cYbb+Tmm29mzz335O6776Zbt24sW7aM8ePHs3Dhwore20svvcQ3v/lN7rnnHrp3786//uu/8p3vfIfJkycze/Zsli5dSkTwyiuvtGSXVZ3hV5IkqZ3MnDmTc889F4DTTz+dmTNnMmzYMO655x7OOeccunYtolnPnj1btN4VK1Zw2mmnsXLlSt54441mL/v1/ve/nwMOOIC5c+fSu3dvdtxxRwYOHMjatWuZPHkyixYtokuXLjz99NMV1/Dwww+zePFiDj74YADeeOMNDjroIN773vfSrVs3Jk6cyPHHH8+4ceNa9N6qzfArSZLUDtasWcO9997LU089RUSwadMmIoLLL7+clFJFVy2oO0/dy3t96Utf4stf/jInnHAC8+fPZ+rUqc2uq7brQ+/evRk/fjwAV1xxBb179+aJJ55g8+bNdOvWbZvlunbtutXJcrV1pJQ46qijmDlz5jbLPPLII8ydO5dZs2Zx1VVXce+99zZbX1sx/EpSJxeXdPylmdLFqaNLkN7xbrnlFj772c/ywx/+cMu4MWPGcP/993P00Udz9dVXc9hhh23V7aFHjx6sW7duS7eH3r17s2TJEvbff39mz55Njx49AFi7di177bUXADNmzKionpNPPpmvf/3r7LzzzlvC6Nq1a+nbty877LADM2bMYNOmTdssV1NTw/e//302b97MCy+8sOUKEaNGjeKLX/wizzzzDPvttx8bNmxgxYoVfOADH2DDhg0cd9xxjBo1iv3226/1O7EKPOFNkiSpHcycOZNPfOITW407+eSTueGGG5g4cSL77LMPgwcP5sADD+SGG24AYNKkSXz0ox/dcsLbZZddxrhx4zj88MPp06fPlvVMnTqVU045hdGjRzfbP7jWrrvuyqhRo+jdu/eWbhJf+MIXmDFjBqNGjeLpp5+me/fu2yx38MEHs++++zJo0CDOP/98hg0bBkCvXr247rrrGD9+PIMHD2bUqFFbriIxbtw4Bg8ezJgxY7jiiitavvOqKFJqv//mhw8fnirtNC1JKtjyK1XHkiVL6N+/f0eXoTbQ0GcbEY+mlIbXn9eWX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJKmddOnShSFDhjBw4EBOOeUUNmzY0Op1nXHGGdxyyy0ATJw4kcWLFzc67/z583nwwQdbvI2amhpeeumlbbZb90YdALfddhvHHXdcRbV2NO/wJkmSslTta2hXcj3snXbaiUWLFgHwqU99iquvvpovf/nLW6Zv2rSJLl26tHjb06dPb3L6/Pnz2WWXXfi7v/u7Fq+7vvHjx3PZZZdx9tlnbxk3a9asLbdI7uxs+ZUkSeoAo0eP5plnnmH+/PmMHTuWv//7v2fQoEFs2rSJCy64gBEjRjB48OAtrawpJSZPnsyAAQM4/vjjWbVq1ZZ1HXbYYdTeSOyXv/wlw4YN48ADD+SII45g+fLlXH311VxxxRUMGTKEX//616xevZqTTz6ZESNGMGLECB544AEA1qxZw9FHH83QoUM5++yzaehmaEceeSRLly5l5cqVAGzYsIF77rmHE088kW984xuMGDGCgQMHMmnSpAaXr9uavHDhQg477DAAXnvtNT73uc8xYsQIhg4dyu233w7A7373O0aOHMmQIUMYPHgwy5Yt2679bviVJElqZ2+99RZ33nkngwYNAuCRRx7h0ksvZfHixVx77bW8733vY8GCBSxYsIBrrrmG5557jtmzZ/P73/+e3/72t1xzzTUNdmNYvXo1Z511FrfeeitPPPEEN998MzU1NZxzzjmcd955LFq0iNGjRzNlyhTOO+88FixYwK233srEiRMBuOSSSzjkkEN4/PHHOeGEE3j++ee32UaXLl046aSTuOmmmwCYM2cOY8eOpUePHkyePJkFCxbw1FNP8frrr3PHHXdUvE8uvfRSDj/8cBYsWMC8efO44IILeO2117j66quZMmUKixYtYuHChfTt27c1u3wLuz1IkiS1k9dff50hQ4YARcvvmWeeyYMPPsjIkSPZd999Abjrrrt48sknt/SRXbt2LcuWLeO+++5j/PjxdOnShQ984AMcfvjh26z/4Ycf5tBDD92yrp49ezZYxz333LNVH+FXX32VdevWcd999/HTn/4UgOOPP57ddtutweXHjx/PBRdcwJQpU5g1axaf/exnAZg3bx6XX345GzZs4OWXX+aAAw7gYx/7WEX75q677mLOnDl861vfAmDjxo08//zzHHTQQVx66aWsWLGCk046iX79+lW0vsYYfiVJktpJ3T6/dXXv3n3L65QSV155Jcccc8xW8/ziF78goul+yimlZucB2Lx5Mw899BA77bTTNtMqWf7ggw9m5cqVPPHEEzz44IPMmjWLjRs38oUvfIGFCxey9957M3XqVDZu3LjNsl27dmXz5s0AW01PKXHrrbey//77bzV///79+chHPsLPf/5zjjnmGKZPn95g8K+U3R4kSZI6kWOOOYYf/OAHvPnmmwA8/fTTvPbaaxx66KHMmjWLTZs2sXLlSubNm7fNsgcddBC/+tWveO655wB4+eWXAejRowfr1q3bMt/RRx/NVVddtWW4NpAfeuihXH/99QDceeed/PnPf26wxojg1FNPZcKECRx33HF069ZtS5DdY489WL9+faNXd6ipqeHRRx8F4NZbb93qfV955ZVb+gk//vjjADz77LN88IMf5B//8R854YQTePLJJ5vafc0y/EqSJHUiEydOZMCAAQwbNoyBAwdy9tln89Zbb/GJT3yCfv36MWjQID7/+c8zZsyYbZbt1asX06ZN46STTuLAAw/ktNNOA+BjH/sYs2fP3nLC2/e+9z0WLlzI4MGDGTBgAFdffTUAF198Mffddx/Dhg3jrrvuYp999mm0zvHjx/PEE09w+umnA7Drrrty1llnMWjQIE488URGjBjR4HIXX3wxU6ZMYfTo0Vtd2eKiiy7izTffZPDgwQwcOJCLLroIgBtvvJGBAwcyZMgQli5duqWLRWtFQ2fhtZXhw4en2jMRJUmVqfblmFqjkks4SZ3dkiVL6N+/f0eXoTbQ0GcbEY+mlIbXn9eWX0mSJGWj2fAbEftHxKI6j1cj4tyI6BkRd0fEsvK54dMBJUmSpE6i2fCbUvp9SmlISmkI8LfABmA2cCEwN6XUD5hbDkuSJEmdVku7PRwB/HdK6Q/Ax4EZ5fgZwIlVrEuSJKnq2vNcJ7WPln6mLQ2/pwMzy9e9U0ory42uBPZsaIGImBQRCyNi4erVq1u4OUmSpOro1q0ba9asMQC/i6SUWLNmDd26dat4mYpvchER7wFOAL7WwqKmAdOguNpDS5aVJEmqlr59+7JixQpsjHt36datW4tuedySO7x9FHgspfRiOfxiRPRJKa2MiD7AqhasS5IkqV3tuOOOW277q3y1pNvDeN7u8gAwB5hQvp4A3F6toiRJkqS2UFH4jYidgaOAn9YZfRlwVEQsK6ddVv3yJKnjRHSOhySpeirq9pBS2gDsXm/cGoqrP0iSJEnvCN7hTZIkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2DL+SJEnKhuFXkiRJ2TD8SpIkKRuGX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlo2tHFyBJkvROF9HRFRRS6ugKOj9bfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJykZF4Tcido2IWyJiaUQsiYiDIqJnRNwdEcvK593aulhJkiRpe1R6tYd/A36ZUvpkRLwH2Bn4OjA3pXRZRFwIXAj8UxvVKUmSpGbEJR1/2Yl0cee+5ESzLb8R8V7gUOBagJTSGymlV4CPAzPK2WYAJ7ZNiZIkSVJ1VNLt4YPAauA/IuLxiJgeEd2B3imllQDl854NLRwRkyJiYUQsXL16ddUKlyRJklqqkvDbFRgG/CClNBR4jaKLQ0VSStNSSsNTSsN79erVyjIlSZKk7VdJ+F0BrEgp/aYcvoUiDL8YEX0AyudVbVOiJEmSVB3Nht+U0v8Af4yI/ctRRwCLgTnAhHLcBOD2NqlQkiRJqpJKr/bwJeD68koPzwL/QBGcb4qIM4HngVPapkRJkiSpOioKvymlRcDwBiYdUdVqJEmSpDbkHd4kSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2DL+SJEnKhuFXkiRJ2TD8SpIkKRuGX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGx0rWSmiFgOrAM2AW+llIZHRE/gRqAGWA6cmlL6c9uUKUmSJG2/lrT8jk0pDUkpDS+HLwTmppT6AXPLYUmSJKnT2p5uDx8HZpSvZwAnbnc1kiRJUhuqNPwm4K6IeDQiJpXjeqeUVgKUz3u2RYGSJElStVTU5xc4OKX0p4jYE7g7IpZWuoEyLE8C2GeffVpRoiRJklQdFbX8ppT+VD6vAmYDI4EXI6IPQPm8qpFlp6WUhqeUhvfq1as6VUuSJEmt0Gz4jYjuEdGj9jVwNPAUMAeYUM42Abi9rYqUJEmSqqGSbg+9gdkRUTv/DSmlX0bEAuCmiDgTeB44pe3KlCRJkrZfs+E3pfQscGAD49cAR7RFUZIkSVJb8A5vkiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2DL+SJEnKhuFXkiRJ2TD8SpIkKRuGX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2Kg6/EdElIh6PiDvK4Z4RcXdELCufd2u7MiVJkqTt15KW3ynAkjrDFwJzU0r9gLnlsCRJktRpVRR+I6IvcDwwvc7ojwMzytczgBOrWpkkSZJUZZW2/H4X+Cqwuc643imllQDl857VLU2SJEmqrmbDb0SMA1allB5tzQYiYlJELIyIhatXr27NKiRJkqSqqKTl92DghIhYDswCDo+InwAvRkQfgPJ5VUMLp5SmpZSGp5SG9+rVq0plS5IkSS3XbPhNKX0tpdQ3pVQDnA7cm1L6NDAHmFDONgG4vc2qlCRJkqpge67zexlwVEQsA44qhyVJkqROq2tLZk4pzQfml6/XAEdUvyRJkiSpbXiHN0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2unZ0AZLeFpdER5dAujh1dAmSJLUZW34lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNpoNvxHRLSIeiYgnIuJ3EXFJOb5nRNwdEcvK593avlxJkiSp9Spp+f0LcHhK6UBgCHBsRIwCLgTmppT6AXPLYUmSJKnTajb8psL6cnDH8pGAjwMzyvEzgBPbokBJkiSpWirq8xsRXSJiEbAKuDul9Bugd0ppJUD5vGcjy06KiIURsXD16tVVKluSpI4R0TkeklqnovCbUtqUUhoC9AVGRsTASjeQUpqWUhqeUhreq1evVpYpSZIkbb8WXe0hpfQKMB84FngxIvoAlM+rql2cJEmSVE2VXO2hV0TsWr7eCTgSWArMASaUs00Abm+jGiVJkqSq6FrBPH2AGRHRhSIs35RSuiMiHgJuiogzgeeBU9qwTkmSJGm7NRt+U0pPAkMbGL8GOKItipIkSZLagnd4kyRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpSNSm5yIb3rRXR0BaWpHV2ApHeKuKTjf3Gli1NHlyC1mC2/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2DL+SJEnKhuFXkiRJ2TD8SpIkKRuGX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNpoNvxGxd0TMi4glEfG7iJhSju8ZEXdHxLLyebe2L1eSJElqvUpaft8CvpJS6g+MAr4YEQOAC4G5KaV+wNxyWJIkSeq0mg2/KaWVKaXHytfrgCXAXsDHgRnlbDOAE9uoRkmSJKkqurZk5oioAYYCvwF6p5RWQhGQI2LPRpaZBEwC2Geffbar2GqI6OgKCil1dAWSJEn5qfiEt4jYBbgVODel9Gqly6WUpqWUhqeUhvfq1as1NUqSJElVUVH4jYgdKYLv9Smln5ajX4yIPuX0PsCqtilRkiRJqo5KrvYQwLXAkpTSd+pMmgNMKF9PAG6vfnmSJElS9VTS5/dg4DPAbyNiUTnu68BlwE0RcSbwPHBKm1QoSZIkVUmz4TeldD/Q2GliR1S3HEmSJKnteIc3SZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdmo5CYXagNxSWOXTm4/6eLU0SVIkiS1K1t+JUmSlA3DryRJkrJh+JUkSVI2DL+SJEnKhuFXkiRJ2TD8SpIkKRuGX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbDQbfiPiRxGxKiKeqjOuZ0TcHRHLyufd2rZMSZIkaftV0vJ7HXBsvXEXAnNTSv2AueWwJEmS1Kk1G35TSvcBL9cb/XFgRvl6BnBidcuSJEmSqq+1fX57p5RWApTPe1avJEmSJKlttPkJbxExKSIWRsTC1atXt/XmJEmSpEa1Nvy+GBF9AMrnVY3NmFKallIanlIa3qtXr1ZuTpIkSdp+rQ2/c4AJ5esJwO3VKUeSJElqO5Vc6mwm8BCwf0SsiIgzgcuAoyJiGXBUOSxJkiR1al2bmyGlNL6RSUdUuRZJkiSpTXmHN0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJyobhV5IkSdkw/EqSJCkbhl9JkiRlw/ArSZKkbBh+JUmSlA3DryRJkrJh+JUkSVI2DL+SJEnKhuFXkiRJ2TD8SpIkKRuGX0mSJGXD8CtJkqRsGH4lSZKUDcOvJEmSsmH4lSRJUjYMv5IkScqG4VeSJEnZMPxKkiQpG9sVfiPi2Ij4fUQ8ExEXVqsoSZIkqS20OvxGRBfg34GPAgOA8RExoFqFSZIkSdW2PS2/I4FnUkrPppTeAGYBH69OWZIkSVL1bU/43Qv4Y53hFeU4SZIkqVOKlFLrFow4BTgmpTSxHP4MMDKl9KV6800CJpWD+wO/b3257yp7AC91dBHqdDwu1BCPCzXE40IN8bh421+nlHrVH9l1O1a4Ati7znBf4E/1Z0opTQOmbcd23pUiYmFKaXhH16HOxeNCDfG4UEM8LtQQj4vmbU+3hwVAv4jYNyLeA5wOzKlOWZIkSVL1tbrlN6X0VkRMBv4L6AL8KKX0u6pVJkmSJFXZ9nR7IKX0C+AXVaolN3YFUUM8LtQQjws1xONCDfG4aEarT3iTJEmS3mm8vbEkSZKyYfhtZ94SWg2JiB9FxKqIeKqja1HnEBF7R8S8iFgSEb+LiCkdXZM6XkR0i4hHIuKJ8ri4pKNrUucREV0i4vGIuKOja+nMDL/tyFtCqwnXAcd2dBHqVN4CvpJS6g+MAr7o7wsBfwEOTykdCAwBjo2IUR1bkjqRKcCSji6iszP8ti9vCa0GpZTuA17u6DrUeaSUVqaUHitfr6P4g+ZdNDOXCuvLwR3LhyfviIjoCxwPTO/oWjo7w2/78pbQklosImqAocBvOrgUdQLlV9uLgFXA3SkljwsBfBf4KrC5g+vo9Ay/7SsaGOd/7JIaFRG7ALcC56aUXu3oetTxUkqbUkpDKO6sOjIiBnZwSepgETEOWJVSerSja3knMPy2r4puCS1JABGxI0XwvT6l9NOOrkedS0rpFWA+ni8gOBg4ISKWU3SpPDwiftKxJXVeht/25S2hJVUkIgK4FliSUvpOR9ejziEiekXEruXrnYAjgaUdWpQ6XErpaymlvimlGopscW9K6dMdXFanZfhtRymlt4DaW0IvAW7yltACiIiZwEPA/hGxIiLO7Oia1OEOBj5D0YKzqHwc19FFqcP1AeZFxJMUDSp3p5S8rJXUAt7hTZIkSdmw5VeSJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/kiRJysb/B7eBjEXsF4YiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.plot(kind = 'bar', figsize=(12,6),color=('blue','green'))\n",
    "plt.title(\"Bar chart of Predicted values and Actual Values\",fontsize = 14)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Making Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.of hours studied : 9.25\n",
      "Predicted Score : 93.6917\n"
     ]
    }
   ],
   "source": [
    "hours = 9.25\n",
    "test = np.array([hours]).reshape(-1,1)\n",
    "ps = Lr.predict(test)\n",
    "print(\"No.of hours studied :\",hours)\n",
    "print(f\"Predicted Score : {ps[0].round(4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So, 93.69% is the score that a student is expected to score after studying 9.25 hours/day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Evaluating the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.18386\n",
      "Mean Squared Error: 21.59877\n",
      "R2-value: 0.94549\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "from sklearn.metrics import r2_score\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Yhat).round(5)) \n",
    "print('Mean Squared Error:',metrics.mean_squared_error(Y_test, Yhat).round(5))\n",
    "print('R2-value:',r2_score(Y_test, Yhat).round(5))"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}