Task #1 Prediction using Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRIP @ The Sparks Foundation - Data Science and Business Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1: Prediction using Supervised Machine Learning\n",
    "\n",
    "### Author: DANIEL JOSEPH RAJ I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective:\n",
    "\n",
    "Predict the percentage of an student based on the number of hours they study per day using Simple Linear Regression technique on the student marks dataset. Also predict the score if a student studies for 9.25 hrs/ day? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing all the required libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"http://bit.ly/w-data\") #Importing data using remote link\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, we can see that there is no missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above table, we can observe that the average study hours of the student is 5.01 hours and the average scores of the student is 51.48\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting Scatter plot between the response vaiable and the exploratory variable\n",
    "\n",
    "plt.figure(dpi=80)\n",
    "plt.scatter(data['Hours'],data['Scores'],color = \"deeppink\")\n",
    "plt.title(\"Marks Vs Study Hours/day\")\n",
    "plt.xlabel(\"No. Of Hours Study/day\")\n",
    "plt.ylabel(\"Scores\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Hours   Scores\n",
      "Hours   1.00000  0.97619\n",
      "Scores  0.97619  1.00000\n"
     ]
    }
   ],
   "source": [
    "## Pearson Correlation\n",
    "\n",
    "correlations = data.corr(method='pearson')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot and matrix, we can see that there is a highly positive linear correlation betweeen the 2 variables. So we can conclude that, the when the no. of study hours increases the percentage scores will also increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Convert the data into array type\n",
    "\n",
    "X = data.iloc[:, :-1]       #attributes\n",
    "y = data.iloc[:,1].values   #labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SPlit data into train and test\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: [9.59563563]\n",
      "Intercept  : 2.8102364628265804\n"
     ]
    }
   ],
   "source": [
    "## Training the algorithm\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "\n",
    "print(\"Coefficient:\", lr.coef_)\n",
    "print(\"Intercept  :\", lr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the model equation is,\n",
    "### Scores = 2.81032 + 9.59563 * (Hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting the Regression Line\n",
    "\n",
    "sns.lmplot(\"Hours\",\"Scores\", data)\n",
    "sns.set_style(\"ticks\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scoring"
   ]
  },
  {
   "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</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>88.211394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>28.718453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>69.020122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>39.273652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>13.365436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      95  88.211394\n",
       "1      30  28.718453\n",
       "2      76  69.020122\n",
       "3      35  39.273652\n",
       "4      17  13.365436"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test = lr.predict(X_test)\n",
    "\n",
    "## Comparing Actual vs Predicted values\n",
    "\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_test})  \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score           : 0.97101\n",
      "Mean Squared Error : 25.58408\n",
      "Mean Absoulte Error: 4.59165\n"
     ]
    }
   ],
   "source": [
    "print ('R2 score           :', round(r2_score(y_test, y_pred_test),5))\n",
    "print ('Mean Squared Error :', round(mean_squared_error(y_test, y_pred_test),5))\n",
    "print ('Mean Absoulte Error:', round(mean_absolute_error(y_test, y_pred_test),5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the score if a student studies for 9.25 hrs/ day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score, When a student studies 9.25 hours per day is equal to 91.57\n"
     ]
    }
   ],
   "source": [
    "test_x = [[9.25]] # X has to be a 2-D array\n",
    "\n",
    "print(f\"Predicted Score, When a student studies {test_x[0][0]} hours per day is equal to\", round(lr.predict(test_x)[0],2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The model equation built to predict the Scores of the students is, Scores = 2.81032 + 9.59563 * (Hours)\n",
    "2. From the estimated co-efficients, we can conclude that, when the study hour per day increases by 1 unit then the marks increases by 9.59563 units.\n",
    "3. Here, the R-squared value is 0.9710, which indicates that 97.1% of the variance of the dependent variable (Scores) is explained by the variance of the independent variable (Study_hours/day).\n",
    "4. If a student studies for 9.25 hours/day, then the predicted score is equals to 91.57"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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