Student_Scores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Simple Linear Rgression**\n",
    "In this Linear Regression task we sill predict the Percentage Score Obtained by a student based on number of hours he study. This is a Simple Linear Regre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries required for this task\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of     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": [
    "df=pd.read_csv(\"student_scores.csv\")\n",
    "df.head"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This dataset contain data about Number of hours a student study per day and score he get in exam "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Hours', 'Scores'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are two columns in dataset: \"Hours\", \"Scores\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2 Columns. Contain 25 values. Column datatype is float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <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": [
    "#Statistical Summary of dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "Y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Score Vs. Hours of Study')"
      ]
     },
     "execution_count": 7,
     "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": [
    "#Score Distribution Plotting\n",
    "plt.scatter(X,Y)\n",
    "plt.xlabel(\"Hours of study\")\n",
    "plt.ylabel(\"Percentage Score\")\n",
    "plt.title(\"Score Vs. Hours of Study\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From above plot we can see that there is positive Correlation between No. of study hours and Percentage score of a student"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: \n",
      "     Hours\n",
      "1     5.1\n",
      "19    7.4\n",
      "9     2.7\n",
      "21    4.8\n",
      "5     1.5\n",
      "2     3.2\n",
      "15    8.9\n",
      "8     8.3\n",
      "14    1.1\n",
      "13    3.3\n",
      "4     3.5\n",
      "23    6.9\n",
      "11    5.9\n",
      "18    6.1\n",
      "0     2.5\n",
      "12    4.5\n",
      "16    2.5\n",
      "6     9.2\n",
      "3     8.5\n",
      "17    1.9 \n",
      "\n",
      " X_test:  \n",
      "     Hours\n",
      "10    7.7\n",
      "24    7.8\n",
      "7     5.5\n",
      "22    3.8\n",
      "20    2.7 \n",
      "\n",
      " Y_train: \n",
      " 1     47\n",
      "19    69\n",
      "9     25\n",
      "21    54\n",
      "5     20\n",
      "2     27\n",
      "15    95\n",
      "8     81\n",
      "14    17\n",
      "13    42\n",
      "4     30\n",
      "23    76\n",
      "11    62\n",
      "18    67\n",
      "0     21\n",
      "12    41\n",
      "16    30\n",
      "6     88\n",
      "3     75\n",
      "17    24\n",
      "Name: Scores, dtype: int64 \n",
      "\n",
      " Y_test: \n",
      " 10    85\n",
      "24    86\n",
      "7     60\n",
      "22    35\n",
      "20    30\n",
      "Name: Scores, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train: \\n\",X_train,\"\\n\\n\",\"X_test:  \\n\",X_test,\"\\n\\n\",\"Y_train: \\n\",Y_train,\"\\n\\n\",\"Y_test: \\n\",Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithm Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Complete\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model1=LinearRegression()\n",
    "model1.fit(X_train,Y_train)\n",
    "\n",
    "print(\"Training Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y =  [9.42094308] X +  3.481588331624316\n"
     ]
    }
   ],
   "source": [
    "#Equation\n",
    "Y_eq=model1.coef_*X_train + model1.intercept_\n",
    "print(\"Y = \",model1.coef_,\"X + \",model1.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,Y_train)\n",
    "plt.plot(X_train,Y_eq)\n",
    "plt.xlabel(\"Hours of study\")\n",
    "plt.ylabel(\"Percentage Score\")\n",
    "plt.title(\"Score Vs. Hours of Study\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>76.022850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>86</td>\n",
       "      <td>76.964944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>55.296775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>39.281172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>28.918135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "10      85  76.022850\n",
       "24      86  76.964944\n",
       "7       60  55.296775\n",
       "22      35  39.281172\n",
       "20      30  28.918135"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred=model1.predict(X_test)\n",
    "df_compare=pd.DataFrame({\"Actual\":Y_test,\"Predicted\":Y_pred})\n",
    "df_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  5.615693536785082\n"
     ]
    }
   ],
   "source": [
    "#Evaluation Metric\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Mean Absolute Error: \",mean_absolute_error(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "What will be predicted score if a student studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([90.62531184])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.predict([[9.25]])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "If a student studies for 9.25 hours/day then he will get 94.18 % Score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank You"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
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