Task-1_Prediction_using_Supervised_ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fc95384",
   "metadata": {},
   "source": [
    "# The Sparks Foundation - Data Science & Business Analytics Intern"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61f4005",
   "metadata": {},
   "source": [
    " # TASK-1: Predict the Percentage of a student based on the no. of hours of study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c92929",
   "metadata": {},
   "source": [
    "# Batch-JANUARY 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb736b87",
   "metadata": {},
   "source": [
    "# Author : SHWETA BHAGAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab6622c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2fa82e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully !\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30\n",
       "5    1.5      20\n",
       "6    9.2      88\n",
       "7    5.5      60\n",
       "8    8.3      81\n",
       "9    2.7      25"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://bit.ly/w-data\"\n",
    "data = pd.read_csv(url)\n",
    "print(\"Data imported successfully !\")\n",
    "\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13378f5f",
   "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": [
    "# Plotting graph between Hours Studied VS Percentage  \n",
    "data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage' ,size=22)  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cab1e7d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing the data\n",
    "X = data.iloc[:, :-1].values  \n",
    "y = data.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fa07cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data into training and test sets\n",
    " \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9d601e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training completed Sucessfully !\n"
     ]
    }
   ],
   "source": [
    "#Training the Algorithm\n",
    "  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training completed Sucessfully !\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60d1f229",
   "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": [
    "## Plotting for the test data\n",
    "plt.title('Linear Regression Plot',size=22)  \n",
    "sns.regplot(x= data['Hours'], y= data['Scores'])\n",
    "plt.ylabel('Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "047950ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "# Testing data - In Hours\n",
    "print(X_test) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f96f3b00",
   "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>Predicted Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.4</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Predicted Scores\n",
       "0    1.5         16.884145\n",
       "1    3.2         33.732261\n",
       "2    7.4         75.357018\n",
       "3    2.5         26.794801\n",
       "4    5.9         60.491033"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making Predictions\n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "prediction = pd.DataFrame({'Hours': [i[0] for i in X_test], 'Predicted Scores': [j for j in y_pred]})\n",
    "prediction[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7162982a",
   "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>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c46120",
   "metadata": {},
   "source": [
    "What will be the Predicted score if a student studies for 9.25hrs/day ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4819d393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours studied: [9.25]\n",
      "Predicted Score = [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "# Predicting score if student studies for 9.25 hours/day\n",
    "hrs = [9.25]\n",
    "result = regressor.predict([hrs])\n",
    "print(f\"No of Hours studied: {hrs}\")\n",
    "print(f\"Predicted Score = {result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec08ac41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "#Evaluating the model \n",
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) #the mean square error"
   ]
  }
 ],
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