Supervised Learning - Predict percent based on hours studied.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction Using Supervised Learning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the percentage of an student based on the no. of study hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all the required libraries\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data with 25  rows and  2  cols\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>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": [
    "#load the dataset to pandas dataframe\n",
    "URL = \"http://bit.ly/w-data\"\n",
    "student_data = pd.read_csv(URL)\n",
    "\n",
    "print(\"Data with\", student_data.shape[0],\" rows and \",student_data.shape[1],\" cols\")\n",
    "student_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "#data visualization\n",
    "\n",
    "\n",
    "# Plotting the distribution of scores\n",
    "student_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above, we can clearly see that there is a positive linear relationship between the number of hours studied and percentage of score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25, 1), (25,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prepare data \n",
    "\n",
    "X = student_data.copy()\n",
    "y = student_data['Scores']\n",
    "del X['Scores']\n",
    "\n",
    "X.shape , y.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape :  (20, 1) (20,)\n",
      "Test data shape :  (5, 1) (5,)\n"
     ]
    }
   ],
   "source": [
    "X_train , X_test ,y_train  , y_test = train_test_split(X,y,test_size=0.2,random_state=1)\n",
    "print(\"Train data shape : \",X_train.shape , y_train.shape )\n",
    "print(\"Test data shape : \",X_test.shape , y_test.shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineareg = LinearRegression()\n",
    "lineareg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([10.46110829]), -1.5369573315500702)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineareg.coef_ , lineareg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "line = lineareg.coef_ * X_train + lineareg.intercept_\n",
    "\n",
    "plt.scatter(X_train,y_train)\n",
    "plt.plot(X_train,line,'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.97026179, 32.98470004, 18.33914843, 87.38246316, 48.67636248])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred = lineareg.predict(X_test)\n",
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>9.970262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>42</td>\n",
       "      <td>32.984700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>18.339148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>87.382463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>48.676362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "14      17   9.970262\n",
       "13      42  32.984700\n",
       "17      24  18.339148\n",
       "3       75  87.382463\n",
       "21      54  48.676362"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Actual':y_test,'Predicted':ypred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 7.882398086270432\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:',mean_absolute_error(y_test, ypred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = [95.22829438]\n"
     ]
    }
   ],
   "source": [
    "# testing with unkown data\n",
    "hours = np.array(9.25)\n",
    "y_pred = lineareg.predict(hours.reshape(-1,1))\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}